At NVIDIA’s GTC 2025 event on Tuesday, the company delivered a variety of new advancements across AI hardware, personal supercomputers, self-driving cars, and humanoid robots. Moreover, the event took an unexpected turn when an unlikely guest made an appearance.

Surely, if Pat Gelsinger was still the CEO of Intel, there’s no way he’d be seen mingling with CEO Jensen Huang at an NVIDIA event. That said, Gelsinger certainly didn’t hold back and offered a few strong takes on the industry. 

He participated in a panel discussion alongside the hosts of the Acquired podcast and several other industry experts. While Gelsinger applauded NVIDIA’s accomplishments in the present era of AI, he disagreed with Huang on certain key issues—specifically, the timeline for the arrival of quantum computing and the use of GPUs for inference. 

‘Data Centres Will Have CPUs, GPUs, and QPUs’

Gelsinger, who is notably bullish on quantum computing, stated that it could be realised within the next few years. 

This stands in contrast to Huang’s comments earlier this year, where he said that bringing “very useful quantum computers” to market could take anywhere from 15 to 30 years. His statements triggered a massive selloff in the quantum computing sector, wiping out approximately $8 billion in market value. 

“I disagree with Jensen,” said Gelsinger, adding that the data centres of the future will have quantum processing units (QPUs) handling workloads, along with GPUs and CPUs. 

Similar to how GPUs are deployed to handle tasks for training AI models in language and human-like behaviour, Gelsinger believes it is only appropriate to have a quantum computing model for the complex parts of humanity. “Most interesting things in humanity are quantum effects,” he said. 

He added that many unsolved problems today run on quantum effects, and quantum computers would help realise many ideas like superconducting, composite materials, cryogenics and medical breakthroughs, among others.

“That’s why this is a thrilling time to be a technologist. I just wish I was 20 years younger to be doing more,” he said. 

While Gelsinger differs from Huang, he shares an optimistic view with Microsoft co-founder Bill Gates and Google. 

“There is a possibility that he (Huang) could be wrong. There is the possibility in the next three to five years that one of these techniques would get enough true logical qubits to solve some very tough problems,” said Gates to Yahoo Finance. 

Besides, even Microsoft and Amazon have already taken major strides in quantum computing within the first three months of the year. On the flipside, Meta CEO Mark Zuckerberg resonated with Huang. “My understanding is that [quantum computing] is still ways off from being a very useful paradigm,” Zuckerberg had said in a podcast episode a few months ago. 

Ironically, NVIDIA does seem to have huge plans for quantum computing. The company announced at the GTC event that it is building a Boston-based research centre to advance quantum computing. 

‘Huang Got Lucky With AI’

Besides, Gelsinger clarified that he isn’t a fan of GPUs for AI model inference—the process in which a pre-trained AI model applies its learnings to generate outputs.

He reflected on the early days when a CPU, or a cluster of them, was the undisputed “king of the hill” for running workloads on computer systems. When Huang decided to use a graphics device (GPU) for the same purpose, Gelsinger said that, in the end, he “got lucky” with AI. 

While he acknowledged that AI and machine learning algorithms demand the GPU architecture, which is where most of the developments are being made today, he also pointed out, “There’s a lot more to be done, and I’m not sure all of those are going to land on GPUs in the future.” 

While GPUs work well for training, Gelsinger added that there needs to be a more optimised solution for inference. “A GPU is way too expensive. I argue it’s 10,000 times too expensive to fully realise what we want to do with the deployment of inference of AI.” 

His sentiments are also reflected by the growing ecosystem of inference-specific hardware that is overcoming the inefficiencies posed by GPUs. Companies like Groq, Cerebras, and SambaNova have achieved tangible and useful real-world results for providing high-speed inference. 

For instance, French AI startup Mistral recently dubbed its app ‘Le Chat’ the fastest AI assistant by deploying inference on Cerebras’ hardware. 

Even Huang has acknowledged this in the past. In a podcast episode last year, he said that one of the company’s challenges is to provide efficient, high-speed inference. Having said that, companies working on AI inference hardware may not compete with NVIDIA after all.  

Jonathan Ross, CEO of Groq, said, “Training should be done on GPUs.” He also suggested that NVIDIA will sell every single GPU they make for training. 

All things considered, Gelsinger’s first outing post-resignation involved several strong statements. However, it remains clear that he’s still a massive fan of Huang and the work NVIDIA has accomplished. 

When DeepSeek made a significant impact on NVIDIA’s stock price, Gelsinger argued that the market reaction was wrong. He also revealed that he is an NVIDIA stock buyer, expressing that he was “happy” to benefit from the lower prices. 

Intel Corporation on Wednesday announced the appointment of Lip-Bu Tan as chief executive officer, effective March 18. He succeeds interim co-CEOs David Zinsner and Michelle (MJ) Johnston Holthaus. Tan will also rejoin Intel’s board of directors after having stepped down in August 2024.

Zinsner will continue as executive vice president and chief financial officer, while Johnston Holthaus will remain CEO of Intel Products. Frank D. Yeary, who served as interim executive chair of the board during the CEO search, will return to his role as independent chair once Tan assumes leadership.

Tan brings over 20 years of semiconductor and software experience and has extensive relationships across Intel’s ecosystem. He previously served as CEO of Cadence Design Systems from 2009 to 2021, leading a transformation that saw the company more than double its revenue, expand operating margins, and achieve a stock price appreciation of over 3,200%.

He served on the Cadence board from 2004 through his tenure as executive chairman from 2021 to 2023. He is also a founding managing partner of Walden Catalyst Ventures and chairman of Walden International. Additionally, he holds board positions at Credo Technology Group and Schneider Electric.

The appointment comes after former CEO Pat Gelsinger, who was ousted last year, had set high expectations for Intel’s manufacturing and AI capabilities but failed to meet them, leading to financial and operational challenges. In its latest earnings report, Intel posted a revenue of $14.26 billion, surpassing the projected $13.81 billion. However, revenue declined 7% year-over-year, marking the third consecutive quarterly decline.

“Lip-Bu is an exceptional leader whose technology industry expertise, deep relationships across the product and foundry ecosystems, and proven track record of creating shareholder value is exactly what Intel needs in its next CEO,” Yeary said. “We are delighted to have Lip-Bu as our CEO as we work to accelerate our turnaround and capitalize on the significant growth opportunities ahead.”

Tan expressed his enthusiasm for the role, stating that he sees significant opportunities to remake Intel’s business in ways that serve their customers better and create value for shareholders. “Intel has a powerful computing platform, a vast customer installed base, and a robust manufacturing footprint that is getting stronger by the day as we rebuild our process technology roadmap,” he said. 

Recently, media reports surfaced that there are ongoing negotiations involving TSMC, Nvidia, and Broadcom regarding a potential joint venture to acquire and operate Intel’s foundry division.

However, in his message to the employees, Tan said that under his leadership, Intel will be “an engineering-focused company.” 

“In many ways, we are the founders of ‘The New Intel.’ We will learn from past mistakes, use setbacks to strengthen our resolve and choose action over distraction to reach our full potential,” said Tan.

“Together, we will work hard to restore Intel’s position as a world-class products company, establish ourselves as a world-class foundry and delight our customers like never before. That’s what this moment demands of us as we remake Intel for the future,” he added.

Yeary acknowledged the contributions of Zinsner and Johnston Holthaus, saying, they’d continue the work to rebuild product leadership and advance our foundry strategy. 

Tan holds a Bachelor of Science in physics from Nanyang Technological University in Singapore, a Master of Science in nuclear engineering from the Massachusetts Institute of Technology, and an MBA from the University of San Francisco. He received the Robert N. Noyce Award from the Semiconductor Industry Association in 2022.

Intel might face a potential split as Broadcom and Taiwan Semiconductor Manufacturing Company (TSMC) explore separate deals that could divide the U.S. chipmaker, according to a recent report by WSJ.

TSMC is looking to acquire a stake in Intel’s wafer foundry services division, with Qualcomm and Broadcom also investing to enhance Intel’s manufacturing capacity. 

Broadcom has examined Intel’s chip design and marketing business and discussed a possible bid with advisers. However, the WSJ reported that the company is unlikely to proceed unless it finds a partner for Intel’s manufacturing unit.

According to the report, TSMC has systematically considered acquiring control of some or all of Intel’s chip plants, possibly through an investor consortium or another structure. The two companies are not working together, and discussions remain preliminary and informal.

Intel’s interim executive chairman, Frank Yeary, has been leading discussions with potential suitors and Trump administration officials. According to WSJ, Yeary stated that his primary focus will be maximising value for Intel shareholders.

The White House has signalled concerns over foreign control of Intel’s US factories. “The administration is unlikely to support a foreign firm operating Intel’s factories,” a White House official told Reuters. 

The official added that while the government supports foreign investment in US manufacturing, national security considerations remain a priority.

The US push to onshore chip manufacturing led by former President Joe Biden’s administration previously benefitted Intel. In November, the U.S. Commerce Department said it was finalising a $7.86 billion subsidy for the company.

Bloomberg reported that Trump’s team recently discussed a deal between TSMC and Intel, with the Taiwanese company showing receptiveness. Intel has struggled in recent years, losing contracts and facing competition from firms like NVIDIA and AMD. 

Former CEO Pat Gelsinger, who was ousted last year, had set high expectations for Intel’s manufacturing and AI capabilities but failed to meet them, leading to financial and operational challenges. In its latest earnings report, Intel posted revenue of $14.26 billion, surpassing the projected $13.81 billion. However, revenue declined 7% year-over-year, marking the third consecutive quarterly decline. The company reported a net loss of $126 million, or 3 cents per share, compared to a net income of $2.67 billion, or 63 cents per share, a year earlier.

Intel is set to face tough questions from investors about its search for a new CEO as it announced its quarterly results on Thursday. 

Revenue came in at $14.26 billion, beating the projected $13.81 billion. However, revenue declined 7% year-over-year, marking the third consecutive quarter of decline. Net loss for the quarter stood at $126 million, or 3 cents per share, compared to a net income of $2.67 billion, or 63 cents per share, a year earlier.

These results come as Intel grapples with falling PC demand, shrinking data centre market share, and uncertainty surrounding its leadership, as last month, it announced the retirement of CEO Pat Gelsinger after a 40-year career. 

Just after, the company announced David Zinsner, executive vice president and chief financial officer, and Michelle Johnston Holthaus, CEO of Intel Products, as interim co-CEOs. 

“Our Q1 outlook reflects seasonal weakness magnified by macro uncertainties, further inventory digestion and competitive dynamics,” said Zinsner during the call. To this, Holthaus added, “Dave and I are taking actions to enhance our competitive position and create shareholder value.”

This raised concerns about the future of its plan to expand into contract chip manufacturing—an initiative strongly backed by Gelsinger.

The chipmaker giant reported a fourth-quarter loss per share of $(0.03) on a GAAP basis, while non-GAAP earnings per share (EPS) stood at $0.13. For the full year, GAAP EPS was deeply negative at $(4.38), with non-GAAP EPS at $(0.13).  

Looking ahead, Intel expects Q1 2025 revenue to be between $11.7 billion and $12.7 billion, signalling further declines. 

The company also said in the release that it continues to lead the AI PC category. It’s on track to ship more than 100 million AI PCs by the end of 2025 and is working with more than 200 ISVs across more than 400 features to optimise its software on Intel silicon. 

No Falcon Shores in the Market Anymore

Intel has officially scrapped plans to bring Falcon Shores to market instead of repurposing it as an internal test chip. The decision comes as the company shifts its focus towards streamlining its roadmap and concentrating resources. This is bound to challenge Intel’s competitive edge in the Indian market compared to other companies like NVIDIA and AMD. 

“We have learned a lot as we have ramped up Gaudi, and we’re applying those learnings going forward,” Holthaus stated during the earnings call. “Based on industry feedback, we plan to leverage Falcon Shores as an internal test chip only without bringing it to market.”  

The company has now acknowledged that expectations for Falcon Shores had already been toned down last month. The move aligns with Intel’s strategy to develop a system-level AI data centre solution at rack scale centred around Jaguar Shores.

Intel’s AI Data Centre Struggles 

Intel sees long-term potential in the AI data centre market but admits it is not where it wants to be today. “This is an attractive market for us over time, but I am not happy with where we are today,” Holthaus said. 

However, the company has yet to establish a meaningful presence in the cloud-based AI data centre market. Intel is focusing on simplifying its AI roadmap and reallocating resources. 

Holthaus also highlighted a broader shift in Intel’s AI strategy, emphasising that AI is not a traditional market but an enabling technology that must integrate seamlessly across computing environments.

In a significant leadership transition, Sridhar Vembu, the founder and long-standing CEO of Zoho Corp (founded in 1996), has announced his decision to step down from the role of CEO. Effective immediately, Vembu will assume the position of chief scientist, focusing on deep research and development initiatives. 

“A new chapter begins today,” he posted on X. “In view of the various challenges and opportunities facing us, including recent major developments in AI, it has been decided that it is best that I should focus full time on R&D initiatives, along with pursuing my personal rural development mission.”

Vembu’s departure from the CEO position marks a pivotal moment for Zoho, a company he established in 1996. Under his leadership, Zoho evolved into a prominent player in the software-as-a-service (SaaS) industry, offering a comprehensive suite of cloud-based applications for businesses worldwide. 

In his new role, Vembu will concentrate on Zoho’s deep R&D initiatives, particularly in the field of AI. “The future of our company entirely depends on how well we navigate the R&D challenge and I am looking forward to my new assignment with energy and vigor. I am also very happy to get back to hands-on technical work,” said Vembu. 

New CEO

Taking over as the new Group CEO is Shailesh Kumar Davey, a co-founder of Zoho and vice president of engineering at ManageEngine, the IT management division of Zoho Corp, who AIM interviewed last year. 

Davey has been instrumental in implementing engineering processes across the organisation and has a background in networking projects from his tenure at Tata-IBM. His extensive experience and leadership within Zoho position him to steer the company through its next phase of growth and innovation. 

ManageEngine has 18 data centres and close to 100 POPs (point of presence), which are small one or two-rack solutions worldwide. They even opened data centres in Canada and Saudi Arabia too. 

“We believe that optimising everything from the software to the server and the data centre can provide significant value to our customers,” said Davey to AIM.

This leadership change comes at a time when Zoho is intensifying its focus on AI and ML. Last year, Zoho, through ManageEngine, invested $10 Mn in NVIDIA, Intel and AMD GPUs, as told to AIM.

Last year, the company had collaborated with Intel to optimise and accelerate video AI workloads, aiming to enhance its AI-powered video security solutions. Shailesh Kumar Davey has been actively involved in these initiatives, emphasising the importance of seamless data management for AI workloads.

Vembu also confirmed that Zoho co-founder Tony Thomas will lead Zoho US, while Rajesh Ganesan will take charge of the ManageEngine division, and Mani Vembu will lead the Zoho.com division.

In a surprising development, Intel has announced plans to spin off its RealSense division as an independent company. The move, set to be completed in the first half of 2025, will make RealSense part of the Intel Capital (ICAP) portfolio.  

Intel RealSense, known for its innovative computer vision and AI depth cameras, has been a niche but impactful segment of Intel’s broader portfolio. 

The announcement follows the launch of the entry-level Intel RealSense Depth Module D421 in September 2024, a product whose future seemed uncertain amid Intel’s financial challenges and corporate restructuring with the retirement of CEO Pat Gelsinger after a 40-year career.  

In a statement to The Robot Report, Intel expressed confidence in the transition:  

“After ten years of incubation, Intel is unleashing the potential of the Intel RealSense computer vision-AI portfolio in a standalone ICAP portfolio company by the first half of 2025. We are committed to ensuring a smooth transition for our customers and continue to provide support throughout the process.”  

A Legacy of Innovation  

RealSense has carved a reputation for delivering low-cost, high-quality depth-sensing technology, making it a popular choice for developers of mobile and industrial robots. 

One high-profile example is ANYbotics’ quadruped robot, ANYmal, which relies on RealSense D435 modules for navigation and terrain traversal.  

The spin-off marks yet another dramatic shift in RealSense’s journey. In 2021, Intel announced plans to shut down RealSense to focus on its core businesses, only to reverse that decision and maintain a scaled-down version of the product line.  

Challenges of Independence

As an independent entity, RealSense faces new uncertainties, including whether it will need to secure external funding to sustain and grow its operations.  

This is not the first time Intel has spun off a business. The company has recently pursued similar strategies, including the spin-off of its foundry business in December 2024 and Mobileye, the autonomous vehicle developer, in October 2022.  

The robotics community is now closely watching to see how RealSense’s independence will impact its customer base and future product innovations. 

Many are hopeful that this move will allow RealSense to focus more narrowly on advancing its technologies, while others remain cautious about the challenges of operating without Intel’s vast resources.  

New Beginning  

Founded in 2014 as an evolution of Intel’s Perceptual Computing division, RealSense has spent over a decade pushing the boundaries of depth-sensing technology. 

With this latest chapter, the industry eagerly awaits the company’s next steps and its potential impact on robotics and AI development.  

For now, questions remain about RealSense’s direction, funding, and the confidence of its existing customers. However, one thing is clear: the spin-off marks a pivotal moment for a division that has already navigated a rollercoaster history.  

As more details emerge, the industry will be watching closely to see how RealSense adapts to its newfound independence.

LG Electronics, last week, introduced its 2025 lineup of gram laptops at the tech event CES 2025, which marked the debut of its first AI-powered devices. The new lineup includes the gram Pro, gram Pro 2in1, gram, and gram Book – all designed to deliver high performance while maintaining the series’ signature slim and lightweight design.

The laptops incorporate LG gram AI, which combines on-device AI and cloud-based AI for enhanced productivity and personalisation. “The 2025 LG gram products, featuring advanced gram AI, are powerful, portable productivity companions that can understand users’ needs, intelligently utilise relevant information, and enhance workflow,” said YS Lee, vice president and head of the IT business unit at LG Electronics.

The AI system features gram chat On-Device, which uses local processing for fast, secure responses, and gram chat Cloud, powered by GPT-4o, to provide more comprehensive knowledge-based insights.

Other notable features include Time Travel, which lets users revisit documents, videos, and web pages quickly, and tools for managing schedules and emails efficiently.

The laptops are powered by Intel’s next-generation processors, the Intel Core Ultra H-Series (Arrow Lake) and Intel Core Ultra V-Series (Lunar Lake). The H-Series focuses on boosting traditional PC performance, while the V-Series offers AI-driven features like real-time video subtitle translation and AI image generation.

The LG gram Pro (17Z90TR) leads the lineup, equipped with an Arrow Lake CPU and NVIDIA GeForce RTX 4050 graphics, making it ideal for demanding tasks such as video editing and 3D rendering. The gram Pro (16Z90TS), the first in the series with Microsoft’s “Copilot+” PC functionality, stands out with its ultra-slim 0.49-inch thickness and 2.73-pound weight.

The LG gram Pro 2-in-1 (16T90TP), which received a CES 2025 Innovation Award, offers versatility with its wirelessly chargeable stylus pen and flexible design. LG is also introducing the LG gram Book (15U50T), an entry-level model aimed at providing the key features of the gram series at a more affordable price point.

All 2025 models feature gram Link 2.0, which simplifies content sharing and file transfers across devices, including iOS and Android smartphones. This feature also allows users to manage phone calls directly from their laptops.

“LG is committed to delivering premium-quality laptops that harness the latest AI technologies to enable users to do and achieve more,” Lee added.

In a surprising turn of events, Intel announced the immediate departure of CEO Pat Gelsinger, marking the end of a tumultuous three-year tenure that saw the company’s stock decline by over 50%. 

Tensions over the company’s strategic direction have been brewing behind the scenes for some time now. According to reports, Intel’s board of directors discussed various transformative options, including private equity transactions and the potential separation of Intel’s factory and product-design businesses, but Gelsinger opposed them. 

Instead, he remained committed to his vision of restoring Intel’s technological edge while developing its foundry services for external clients.

The departure/ouster appears to stem from fundamental disagreements about Intel’s future trajectory. Olivier Blanchard, research director at Futurum Group, in an interview with Yahoo Finance, noted that Gelsinger viewed his role as a “passion project” with a clear vision for the company’s direction. He further said that there were evidently “differences in opinions about where the company needed to go,” according to industry analysts.  

The split between Intel’s foundry and design businesses emerged as a particularly contentious issue, likely serving as a catalyst for the leadership change. 

Erik Stromquist, an investment advisor, told a news publication that this is not good news because ‘Pat was the strategy.’

The timing of this transition is particularly crucial as Intel faces multiple strategic challenges, including catching up in the AI chip race, where competitors like NVIDIA have taken a commanding lead. The company is also navigating complex manufacturing decisions, exemplified by its recent move to have its new Core Ultra chips manufactured by TSMC rather than its own facilities. 

Two CEOs? What Does That Mean?

David Zinsner and Michelle Johnston Holthaus have been appointed interim co-CEOs, bringing complementary strengths to the leadership table. Zinsner, with over 25 years of experience in finance and operations within the semiconductor sector, continues his role as CFO while taking on additional responsibilities. 

Holthaus, a nearly three-decade Intel veteran, assumes the newly created position of CEO of Intel Products, overseeing crucial divisions including client computing, data centre and AI, and network and edge groups. 

The immediate focus for the interim leadership appears twofold. First, they must reassure the market about Intel’s stability and direction. As Blanchard notes, “The very first step is to hopefully assure the market that everything is going smoothly; everything is on the right track”.

Second, they face the challenge of accelerating Intel’s transformation. The company’s board has emphasised the need to strengthen the product portfolio and advance manufacturing capabilities. Frank Yeary, Intel’s interim executive chair, has made it clear: “Returning to process leadership remains central to our mission as we deliver for our customers and restore investor confidence.”

The new leadership faces several immediate challenges. Intel’s Q2 2024 results revealed concerning trends, with revenue declining 0.9% year-over-year and a net loss of $1.61 billion. The company’s struggles in the AI chip market, where NVIDIA has established dominance, have become particularly acute. 

The interim leadership must navigate crucial strategic decisions, including the potential separation of Intel’s foundry and design businesses, a move that Gelsinger had opposed but might now be back on the table. This structural change could prevent regulatory concerns and provide a substantial cash injection for the company. 

Jo-Ellen Pozner, professor at UC Berkeley’s Haas School of Business, suggests that the board’s decision signals a serious commitment to strategic change: “Companies often choose to make big statements like this because they know Wall Street will respond favourably.” 

As expected, Intel’s shares rose over 4% in premarket trading following the announcement. However, they ended down by 0.5%, suggesting that there is no quick fix for Intel. 

What to Expect from the Next CEO?

As the company lurches forward, advancing Intel’s manufacturing capabilities and restoring its technological edge remain crucial, particularly with the development of the Intel 18A node, including optimising the massive investments in new manufacturing facilities.

While Intel repositioned itself in the AI chip market, falling behind NVIDIA, the new CEO must address Intel’s modest $500 million revenue goal for its Gaudi AI product line and develop a more aggressive strategy to compete in this rapidly growing sector. 

Restoring investor confidence remains paramount, as evidenced by the stock’s 52% decline in 2024. Yeary emphasises that the next leader must prioritise putting the product group at the centre of operations while maintaining the company’s commitment to manufacturing competitiveness. 

Intel Corporation today announced the retirement of CEO Pat Gelsinger after a 40-year career, effective December 1, 2024. Gelsinger has also stepped down from the company’s board of directors.

David Zinsner, executive vice president and chief financial officer, and Michelle Johnston Holthaus, CEO of Intel Products, have been appointed interim co-CEOs. Holthaus will continue in her newly created role as CEO of Intel Products, overseeing the Client Computing Group, Data Center and AI Group, and Network and Edge Group.

Frank Yeary, previously the independent chair of Intel’s board, will serve as interim executive chair during the leadership transition. The company’s board has formed a search committee to identify Gelsinger’s permanent successor.

“Leading Intel has been the honor of my lifetime – this group of people is among the best and the brightest in the business, and I’m honored to call each and every one a colleague. Today is, of course, bittersweet as this company has been my life for the bulk of my working career,” said Gelsinger. 

Yeary remarked on Gelsinger’s contributions, saying, “Pat helped launch and revitalise process manufacturing by investing in state-of-the-art semiconductor manufacturing while working tirelessly to drive innovation throughout the company.”

No Pat, No Glory 

Earlier, in an effort to turn around the company’s fortunes, Gelsinger urged the team to build on the momentum in the Foundry business as they approached the launch of Intel 18A.

Moreover, Intel recently partnered with AMD to form the x86 Ecosystem Advisory Group. The advisory group will focus on expanding the x86 ecosystem by simplifying software development and improving platform interoperability.

To compete with NVIDIA and capitalise on the generative AI wave, Intel began manufacturing GPUs, however, unlike NVIDIA, it has struggled to gain market share. According to a recent report, NVIDIA holds 88% of the GPU market share, while Intel is struggling with a minuscule share.

Intel recently launched its AI accelerator, Gaudi 3. In 2025, its successor, Falcon Shores, will combine the AI capabilities of Gaudi with Intel’s powerful GPUs in a single package. By 2026, Intel plans to introduce another version of the AI accelerator super chip, Falcon Shores 2, which is likely to be called Jaguar Shores.

Intel without Pat 

Intel’s board emphasised the importance of prioritising its product group to meet customer demands and advance process and product leadership. Yeary said that the company remains focused on simplifying its product portfolio, enhancing manufacturing and foundry capabilities, and improving profitability.

Gelsinger, who started at Intel in 1979 and served in roles including chief technology officer, reflected on the challenges and progress during his tenure. “It has been a challenging year for all of us as we have made tough but necessary decisions to position Intel for the current market dynamics,” he said.

Zinsner and Holthaus expressed their commitment to building on Intel’s progress. “We are grateful for Pat’s leadership,” they said in a joint statement. “We will redouble our commitment to Intel Products and meeting customer needs.”

Zinsner joined Intel in 2022 after a career at Micron Technology and other leadership roles in the semiconductor industry. Holthaus, a 30-year veteran of Intel, has held multiple senior management positions, including chief revenue officer and general manager of the Client Computing Group.

Intel’s Foundry leadership structure remains unchanged during the transition.

This month, Intel announced the establishment of its AI PC Experience Development Centres at two premier institutions: the Indian Institute of Science (IISc) Bengaluru and the Indian Institute of Technology Hyderabad (IITH). With this, it aims to equip students and researchers with state-of-the-art resources and mentorship to innovate AI applications with significant potential across diverse sectors.

At the core of this initiative are Intel’s latest Core Ultra Processors, featuring an integrated combination of CPU, GPU, and NPU to efficiently manage AI workloads on PCs. Through this, Intel seeks to redefine how PCs are used—enhancing productivity, creativity, security, gaming, and more—while broadening access to AI tools for students and researchers.

At the Bengaluru Tech Summit 2024, Gokul Subramaniam, president of Intel India and VP of the Client Computing Group, spoke about the company’s focus on India as one of its key markets, outlining its future plans. Intel’s vision is ambitious: It aims for 100 million AI PCs in the market by 2025, with 40 million units already projected for completion by the end of 2024. 

What Will the Impact Be?

The development centres at these institutes are equipped with advanced hardware and software tailored for AI research and development. Beyond infrastructure, Intel actively supports students and faculty through collaboration and mentorship. 

Its technologists provide hands-on guidance to help build proof-of-concept applications and prototypes across various fields, fostering practical experience in real-world AI solutions. “Our technologists are mentoring students, faculty, and departments to develop AI PC concepts and explore diverse applications,” Subramaniam said.

Discussing this development with AIM, Govindan Rangarajan, director of IISc Bangalore, said that researchers will be exposed to the latest computational frameworks (hardware and software) in AI. “This can spur research in several fields, specifically when it comes to education, which can be teaching coding to young children. This partnership can also help accelerate the adoption of AI in basic sciences,” Rangarajan said.

He also revealed that Intel has agreed to provide 10 AI PCs to IISc, but more will be made available as and when the need arises.

Apart from the development centre, Intel also worked with IISc to set up four different research programmes focused on computer architecture, LLMs, memory performance, and networking. “These are fundamental things that are very important for the next generation of AI in India,” said Subramaniam.

Highlighting the importance of IISc, Subramaniam said that the most important aspect of the university is not its students but the multidisciplinary and interdisciplinary nature of the campus. “You have mechanical and civil engineers, robotics, architecture design, and a number of other aspects that come together to make AI more pervasive,” he said. 

Likewise, AMD has also been actively looking at Indian universities to establish its presence and upskill students. Last year, it partnered with IISc to advance AI and HPC research in India.

Gilles Garcia, senior director business lead, data centre communication group at AMD, told AIM, “We have a strong relationship with universities. We provide additional training to students and then bring them onto a very established internship programme.” He explained how AMD has established an excellent pipeline for engineering graduates in India. 

AMD’s AI strategy in India follows ground-up and top-down approaches. “We always monitor the value we can deliver and the value others can bring to us,” he said about investing in AI startups in India. 

Last year, AMD pledged to invest $400 million in India over five years in R&D, but Lisa Su, AMD’s CEO, on her recent visit to IISc said, “It’s very likely we will reach that sooner, given the rate and pace of investment and innovation that is going on.” 

She announced that AMD is also building two supercomputers in partnership with the Indian government, which will be announced soon.

Population-Scale Impact

Rangarajan said that as AI is increasingly being adopted in the industry, it is important for scientists to have access to the latest industrial advances to pursue impactful research. “AI can be a game-changer for India. To achieve this potential, it is important that the private sector drives investment into research through manpower, data, and computation,” he added.

When it comes to Intel, Subramaniam explained that one of the key challenges that these programmes are trying to solve is to build population-scale solutions that can leverage AI and have a meaningful impact on people in various segments of society. “Not only consumers or enterprises, but even governments, smart cities, transportation, and agriculture—a lot of these areas which can benefit from AI—can be sorted and integrated into these campuses,” he added.

In the same way, Subramaniam said that if we could bring this equitable technology to every school in Bengaluru, including government schools, it would have an even greater impact. 

“India has over 300 million students, with about 10-20 million enrolled in government and private schools in Karnataka alone,” Subramaniam said. He believes that giving these students access to technology in the labs as well as personal computing devices, IoT devices, or science projects is key to India’s future leadership. 

Besides building AI PCs and solutions, Intel is also undertaking skill development work with the governments of the Indian states. “We conducted the AI readiness programme across the nation and are really excited to be able to continue that and do it in a much more amplified manner in the local ecosystem in Karnataka,” added Subramaniam.

Intel, with the government of Karnataka through the Nipuana Karnataka project, wishes to offer AI PCs to every student from primary to K-12. “If universities can adopt it, even government schools can adopt it.” He said that the same learnings from universities should be applied to schools. 

The Nipuna Karnataka project announced by the Government of Karnataka last week aims to upskill 100,000 professionals and create jobs for at least 70% of them by 2025. To support this initiative, Karnataka has partnered with leading global tech companies including Microsoft, Intel, Accenture, and IBM. 

For the last few years, Intel has been working with various institutions in India under the Unnati AI Labs programme. It provides students with AI PCs and prepares them for the future of generative AI with certifications. 

Moreover, Intel Xeon 6 processors also power the data centres in the country. Intel’s work has contributed to nurturing and building small design companies in the city and the state. “And a lot of that has been nurtured out of the talent that we have built in Bangalore,” added Subramaniam.

When discussing AI, one often thinks about excellent software and intelligent programs. Behind the scenes, however, is a vast world of hardware that makes it all possible. Think of AI hardware and chip-making companies as the backstage crew of a big production, ensuring everything is perfectly aligned so AI can shine. 

Big tech companies like NVIDIA and AMD make special chips that power everything from driverless cars to smart gadgets. 

Companies are always trying to build faster and more powerful chips because AI needs a lot of power to perform its job seamlessly. The race to make the best chip intensifies as AI becomes more advanced. Every tiny improvement makes a big difference.

However, starting a hardware company is no easy feat. 

In India, a Surat-based company, Vicharak, took on the herculean task of churning out hardware in-house designed specifically for AI workloads. The company recently secured funding of ₹1 crore, boosting its valuation to ₹100 crore. 

We’ve received ₹1 crore in funding at a ₹100 crore valuation. We’ve often heard that doing hardware is hard in a city like Surat or even in India. We’ve gone through hundreds of failed prototypes and iterations in our labs, but somebody has to start at some point, right?

This…

— Vicharak (@Vicharak_In) June 25, 2024

Speaking with AIM, founder and CEO Akshar Vastarpara said that Vicharak’s focus is on creating hardware and redefining computing technology. 

“Our first target is to develop a GPU-like technology that can be used in mobile phones, laptops, and servers. We are approaching this in a very different way, starting with the consumer base but scaling to servers and lower-level areas as well,” Vastarpara explained.

This approach led to the creation of Vaaman, a compact computing board featuring a six-core ARM CPU and a field-programmable gate array (FPGA) with 1,12,128 logic cells. Its unique design handles challenges beyond current products, offering 300-Mbps FPGA-CPU connectivity for superior hardware acceleration and parallel computing.

The unique condition garnered a lot of attention on social media.

This is such an insane white pill moment for me.

Back in college I used to such reviews by western YouTubers of western tech companies.

Now time watching an Indian YouTuber make review about a deep tech product of an Indian company.

We’re gonna make it folks. pic.twitter.com/RwkOcxMzmK

— Varsh (@infinite_varsh) June 26, 2024

In this article, AIM explores the importance of AI chips and the most effective strategies that have enhanced their performance in 2024.

The Inference Power Players

In the race to power AI applications, inference chips are the unsung heroes driving real-time decisions, from chatbots to recommendation engines. These specialised processors are the backbone of modern AI, delivering speed and efficiency where it matters most.

To further extend creative possibilities, NVIDIA rolled out its highly anticipated H200 Tensor Core GPU, a successor to the H100, designed for generative AI and high-performance computing workloads. It introduced a faster memory (HBM3E) for improved efficiency​. 

Then came the B100 GPU, which utilised the new Blackwell architecture to focus on AI training and inference. This chip is tailored for AI training and inference, continuing NVIDIA’s focus on accelerating AI advancements​. 

Earlier this year, NVIDIA launched its GH200 chip, combining a GPU and an ARM-based CPU. By October, OpenAI announced receiving the first engineering builds of NVIDIA’s DGX B200 on X.

Notably, NVIDIA CEO Jensen Huang personally delivered the first GPU chip to Elon Musk and presented the first DGX H200 to OpenAI’s Sam Altman and Greg Brockman.

In a similar vein, Microsoft announced that its Azure platform became the first cloud service to implement NVIDIA’s Blackwell system, featuring AI servers powered by the GB200.  NVIDIA reported generating a record-breaking $22.6 billion in data centre revenue this year, a 23% sequential and 427% year-over-year growth, fueled by demand for the Hopper GPU platform. During the earnings call, Huang hinted at future advancements, stating, “After Blackwell, there’s another chip. We’re on a one-year rhythm.”

On the other hand, Google’s parent company Alphabet released two notable AI chips, including the Cloud TPU v5p. The chips were specifically engineered for training LLMs and GenAI with each TPU v5p pod containing 8,960 chips and a bandwidth of 4,800 Gbps. Google also launched Trillium, a high-performance chip for AI data centres offering nearly five times the speed of its predecessor TPU v5e.

​Both chips are integral to Google Cloud’s AI infrastructure, reinforcing Alphabet’s competitive edge in the AI chip market alongside its broader investments in custom hardware.

AMD announced the MI325X AI chip and introduced the series in June 2024. The company created its next generation of Epyc and Ryzen processors and released its latest product — the Zen 5 CPU microarchitecture.

The company launched the MI300A and MI300X AI chips. The MI300A combines a GPU with 228 compute units and 24 CPU cores, while the MI300X is a GPU model featuring 304 compute units. The MI300X and Nvidia’s H100 compete in memory capacity and throughput.

AIM earlier talked about the integration of AMD’s EPYC CPUs with NVIDIA’s HGX and MGX GPUs, which enriches AI and data centre performance while supporting open standards for greater flexibility and scalability.

Similarly, AWS has switched its focus from cloud infrastructure to chips. Its Elastic Compute Cloud Trn1 instances are purpose-built for deep learning and large-scale generative models. They function using AWS Trainium chips and AI accelerators.

The trn1.2xlarge instance was the first iteration. It only had one Trainium accelerator, 32 GB of instance memory and 12.5 Gbps network bandwidth. Now, Amazon has introduced the trn1.32xlarge instance, which has 16 accelerators, 512 GB of instance memory and 1,600 Gbps ability. The company is planning to roll out its latest AI chip, Trainium 2, in the upcoming month. As the Financial Times reported, the chip will likely support targeting AI model training at scale.

“The second version of Trainium – Trainium 2 – will start to ramp up in the next few weeks, and I think it’s going to be very compelling for customers on a price-performance basis,” said former AWS chief Andy Jassy. 

The report further revealed that Amazon’s other AI chip, Inferentia, saves customers approximately 40% on costs for generating responses from AI models. 

In a bid to keep pace with the growing demand for semiconductors capable of training and deploying large AI models, Intel announced its latest AI chip Gaudi 3 at Intel Vision 2024.

The chip, first revealed by CEO Pat Gelsinger at the Intel AI Everywhere event, has double the power efficiency of its predecessor and is capable of running AI models 1.5 times faster than NVIDIA’s H100 GPU. 

It offers various configurations, including a bundle of eight Gaudi 3 chips on one motherboard or a card that can be integrated into existing systems.

Gaudi 3, built on a 5 nm process, signals Intel’s use of manufacturing techniques. According to Gelsinger, Intel plans to manufacture AI chips, potentially for external companies, at a new Ohio factory, which is expected to open in the upcoming years.

On the Edge of Innovation

Training and edge AI chips are the secret sauce fueling AI’s learning process, whether in the cloud or directly on your device. These chips transform raw data into actionable intelligence, driving AI’s next big leap.

American AI company Cerebras Systems, in collaboration with Abu Dhabi-based AI holding company G42, announced the development of Condor Galaxy 3 (CG-3), the latest addition to their AI supercomputing constellation, in 2024.

CG-3 features 64 of Cerebras’ newly launched CS-3 systems, each powered by the WSE-3 chip. It will be available by Q2 2024 and is set to deliver eight exaFLOPs of AI computing power. This marks the third generation of AI supercomputers released by Cerebras Systems in collaboration with G42.

The CS-3 chip, also unveiled by Cerebras, boasts 4 trillion transistors and offers 125 petaflops of peak AI performance per chip. The WSE-3 is designed to double the performance of its predecessor while maintaining the same power consumption and price, making it ideal for training the industry’s most significant AI models.

This announcement follows the release of the second phase of the Condor Galaxy supercomputer, known as Condor Galaxy 2, last November. 

Apple Neural Engine, specialised cores based on Apple chips, furthered the company’s AI hardware design and performance. The neural engine led to the M1 chip for MacBooks. Compared to the generation before, MacBooks with an M1 chip are 3.5 times faster in general performance and five times faster in graphic performance.

After the success of the M1 chip, the company announced further generations. As of 2024, Apple released the M4 chip, but it is only available in iPad Pro. The M4 chip has a neural engine that is three times faster than the M1 chip and CPU that is 1.5 times faster than the M2.

“The new iPad Pro with M4 is a great example of how building best-in-class custom silicon enables breakthrough products,” said Johny Srouji, Apple’s senior vice president of hardware technology. 

After the success of its first specialised AI chip, Telum, IBM introduced its Telum II Processor in August. This processor is designed to power the next-generation IBM Z systems. In addition, IBM unveiled the Spyre Accelerator at the Hot Chips 2024 conference. These chips are likely to become available in 2025.

Clearly, they are determined to design a powerful successor that can outpace its competitors.

Currently, IBM is working on the NorthPole AI chip, which does not have a release date. 

“I’ve bet the whole company on 18A,” Intel CEO Patrick P. Gelsinger said in an interview earlier this year. This statement highlighted the critical importance of Intel’s 18A process technology, not just for the company’s future but potentially for the entire chip industry. 

Intel 18A represents the culmination of the company’s ambitious “five nodes in four years” strategy, which aims to regain process leadership by 2025. This manufacturing process uses technologies like RibbonFET gate-all-around transistors and PowerVia backside power delivery, from which Intel promises significant improvements in performance and efficiency. 

It’s not just about technological advancement; it’s about reclaiming Intel’s position as a leader in chip manufacturing and establishing a strong foothold in the foundry business. This is their biggest innovation since Intel introduced FinFETs to HVM in 2011. Intel Foundry Services (IFS) is banking on 18A to attract customers and compete with industry giants like Taiwan Semiconductor Manufacturing Company (TSMC) and Samsung. 

However, 18A’s potential extends beyond consumer electronics. Intel has secured contracts with the US Department of Defense for the RAMP-C program, which aims to establish a domestic source for leading-edge semiconductor manufacturing. Boeing and Northrop Grumman have recently joined this program, highlighting the strategic importance of 18A for national security. 

Why is it critical for Intel’s Foundry Business?

Intel, once the undisputed leader in semiconductor manufacturing, found itself in the position of relying on its competitor, TSMC, for chip production. This dependence was a result of Intel’s struggles with its own 7nm process node, which allowed TSMC to take the lead in advanced chip manufacturing. As one Hacker News user noted, “Intel flopped so hard on process nodes for four years up until Gelsinger took the reigns… it was honestly unprecedented levels of R&D failure.”

Intel faced a major setback in its 7nm chip production due to a “defect mode” in the process, pushing back the release of 7nm chips from late 2021 to early 2023. As a result, Intel’s yields for 7nm chips fell roughly a full year behind schedule and it has now been renamed to Intel 4. 

Hence, the 18A process is Intel’s opportunity to regain its technological edge and reduce its reliance on external foundries. By successfully implementing both RibbonFET gate-all-around transistors and PowerVia backside power technology, Intel aims to leapfrog competitors and attract high-profile customers. 

Due to these improvements and continuous efforts, Intel has signed multiple deals with big tech companies including Amazon Web Services (AWS), which has entered into a multi-year, multi-billion-dollar contract with Intel. 

This deal includes the production of an AI fabric chip for AWS using Intel’s 18A process, as well as a custom Xeon 6 chip on the Intel 3 process. This collaboration aims to enhance AWS’s cloud infrastructure and AI capabilities, empowering joint customers to run any workload and unlock new AI capabilities. 

The partnership also has strategic implications, supporting the growth of a sustainable domestic AI supply chain in the US. As part of the deal, AWS plans to invest $7.8 billion to expand its data centre operations in Central Ohio. 

Microsoft is another big customer besides AWS. While the specific product hasn’t been identified, it could be related to Microsoft’s recently announced plans for custom-designed chips, including a computer processor and an artificial intelligence accelerator. 

At Intel Foundry Direct Connect, Satya Nadela, CEO of Microsoft, mentioned, “We are in the midst of a very exciting platform shift that will fundamentally transform productivity for every individual organisation and the entire industry.” 

While explaining further, Nadela mentioned that to achieve this vision, Microsoft needs a reliable supply of the most advanced, high-performance and high-quality semiconductors. That’s why Microsoft chose a chip design that we plan to produce on the Intel 18A process. The deal with Microsoft is substantial, with Intel Foundry’s expected lifetime deal value being greater than $15 billion in total across its wafer and advanced packaging segments.

Execution Delays Overshadow Ambition 

TSMC says 18A is similar to its N3P node, and Intel says it is similar to TSMC’s N2 node. The truth is probably somewhere in between, in which case it is going to be a good node. A Reddit user mentioned that Intel’s challenge will be making its price competitive with TSMC’s offerings and actually being able to produce vast volumes of it if they ever wish to attract a whale client like Nvidia or Qualcomm.

Apparently, Intel has a negative reputation for overpromising and underdelivering. A Reddit user mentioned that Intel has damaged its credibility for decades through its use of salami tactics—admitting things bit by bit only when they become undeniable. 

This behaviour is especially evident when it comes to nodes, processes, yields, and general foundry-related matters. They’ve consistently harmed their own trustworthiness with their statements. They repeatedly announce new plans suddenly when deadlines approach, alongside their endless series of delays.

On the other hand, Intel’s position as a leading-edge foundry would make it the most strategically important company in America. If the US government wanted to build AI research facilities, Intel would likely get the contract because its headquarters, leading-edge foundries, and all of its leading-edge research are based in the US, safe from China and North Korea. 

Divesting from fabrication plants (fabs) would be a huge mistake as they would forever be competing with AMD, Apple, and Nvidia on TSMC wafer allocation. TSMC, by all means, would raise the price with a lack of competition from Samsung.

The company expects external customers to tape out their first 18A designs in the first half of 2025, with enterprise production anticipated in early 2026. 

Intel is wading in troubled waters. Yet, in recent months, it has made several attempts to reposition itself in the market by launching new products and exploring strategic partnerships to enhance its competitiveness.

Recently, Intel announced an AI cloud service, Tiber AI Cloud, powered by its new Gaudi 3 accelerator chips. The new offering is designed for enterprises and AI startups looking to leverage powerful cloud resources for scalable AI development and deployment.

However, this, in turn, will put Intel in direct competition with hyperscalers such as Amazon Web Services (AWS), Microsoft Azure and Google Cloud. Interestingly, these hyperscalers are among the biggest customers of NVIDIA, the company known for its advanced GPUs that Gaudi 3 is being positioned against.

Competing with Hyperscalers?

So, is Intel aiming to compete with the hyperscalers? Not exactly. Intel does not intend to become the next hyperscaler or establish data centres across the globe. Instead, its focus is solely on the AI cloud space, where it will compete with the hyperscalers.

This pivot may also reflect Intel’s efforts to explore new business opportunities during a challenging period. This year, the company’s stock has dropped over 60%; however, Intel has said that currently, their focus is on AI compute only.

Instead, the pivot to enterprise AI cloud has come from growing demand from customers. Markus Flierl, corporate vice president, developer cloud, has told CRN that Tiber AI Cloud is a response to the growing demand from customers.

Intel said besides Gaudi 3, Intel will provide Gaudi 2 chips, Xeon CPUs, Core Ultra 200V CPUs to its customers as a part of its AI cloud offerings.

For some time now, Intel has asserted that for most enterprise AI use cases that don’t involve training large language models, high-end, expensive GPUs are unnecessary, and a robust stack of CPUs can be sufficient.

However, the hyperscalers have stacked their data centres with high-end NVIDIA GPUs, and at this moment, it seems like this is what most customers want.

While NVIDIA does continue to dominate the market, it does not mean there are no takers for Intel’s AI chips. For instance, Bhavish Aggarwal’s Krutrim—India’s first generative AI unicorn—has leveraged Intel’s Gaudi 2 chips to train its AI models.

Similarly, other Indian companies such as IT giant Tech Mahindra and Infosys have announced a partnership with Intel to use their hardware for AI. Recently, Inflection AI also announced a partnership with Intel to launch a new enterprise Al system called Inflection for Enterprise. 

Gaudi 3 vs NVIDIA

AI cloud also seems like a good strategy to get Gaudi 3 out in the market and get customers leveraging them. Intel’s Gaudi AI accelerators are seen as a challenger to NVIDIA’s dominance in the AI chip market. 

For Intel, convincing AI companies to switch from NVIDIA GPUs to Gaudi 3 for model training may be a hard sell. Therefore, an AI cloud solution seems like a logical move.

Launched earlier this year, Gaudi 3 represents Intel’s ambitious push into the rapidly growing AI computing space. According to Intel, Gaudi 3 can deliver up to 70% faster training times for large language models like Llama 2 and GPT-3. 

For inference tasks, Gaudi 3 is said to match or outperform the memory-rich H200 in certain scenarios, particularly with larger output sequences.

Many enterprises and AI startups do not possess the resources to acquire these high-end NVIDIA GPUs. Given the constraint in resources, they look for the most cost-effective solutions and Gaudi 3 chips are relatively cheaper compared to NVIDIA’s H100 GPUs.

Moreover, with Tiber AI Cloud, Intel is likely to begin renting out its AI chips at an hourly rate. To attract many startups and enterprises, the company will need to offer its AI chips at a lower price than the hyperscalers, making it an appealing option.

For instance, Indian AI cloud companies like E2E Networks and Yotta offer NVIDIA’s H100 GPUs at a competitive rate of approximately INR 400-500 per hour, making these GPUs accessible to Indian enterprises and startups.

Stabilising a Rocking Ship?

Intel has been trying to catch up to NVIDIA for a considerable time, after failing to capitalise early on the surge in AI-specific chips, like the latter did.

Additionally, Intel has encountered substantial delays and challenges in its chip manufacturing processes, enabling rivals like TSMC to gain an advantage in advanced chip production. Notably, Gaudi 3 is reportedly based on TSMC’s 5 nm node.

Reports from last month also suggested that Broadcom is in talks to acquire Intel, or at least, a part of it. In Q2 2024, Intel also reported a $1.6 billion loss.

With Gaudi 3, Intel hopes to bring some respite to the company and help steady the boat. However, NVIDIA is not the only company Intel is competing with.

In recent years, a wave of AI chip startups has emerged, creating chips that, in many cases, outshine NVIDIA’s high-end GPUs.

For inference tasks, D-Matrix, a startup founded by Sid Sheth, is developing silicon which works best at inferencing tasks. Its flagship product, Corsair, is specifically designed for inferencing generative AI models (100 billion parameter or less) and is much more cost-effective, compared to GPUs.

Groq, another AI chip startup, founded by Jonathan Ross in 2016, claims their AI chips are ten times faster, ten times cheaper, and consume ten times less power.

While challenges persist, it remains to be seen whether Intel’s AI cloud will achieve broader adoption and how Gaudi 3 chips will perform in comparison to NVIDIA.

Intel is all over the place, with Apollo considering a $5 billion investment to save them, while Qualcomm is eyeing an acquisition. But Intel needs no one—under Pat Gelsinger’s leadership, it’s determined to remain independent and emerge stronger. 

“I think Intel will become a shadow of itself. It will go the way of Motorola and BlackBerry. Right now the vultures are coming for the carcass. Apollo has jumped in. They are a well-known distressed buyout shop. The Apollo team specialises in these asset plays. First, they will make a $5b direct investment. Maybe take 1-2 board seats, buy first rights to 2-3 core assets and begin the divestiture playbook,” said Vijar Kohli co-founder of Golden Door. 

Intel seems to be at a crossroads, and nothing is going their way. Recent reports indicate that Qualcomm, having recently entered the PC processor market, is exploring a potential takeover of Intel, renowned for its CPUs and X86 architecture. However, the deal is described as “far from certain” and would likely face significant regulatory scrutiny.

With Intel’s acquisition, Qualcomm is possibly trying to strengthen its hold in the PC market and add Intel’s Lunar Lake with x86 architecture in its portfolio. “The interests that Qualcomm might have in Intel would be strictly on the design side, on the chip designs for PCs. That’s still a huge market. And just Intel and AMD are players there, and it’s likely to remain the dominant form of technology for PC CPUs,” said JoAnne Feeney, Advisors Capital Management portfolio manager.

Surprisingly, when Microsoft recently announced its Copilot + AI PCs, it actively showed love for Qualcomm processors more than for Intel and AMD, as the capabilities of the current generation of both companies were not even close to what Qualcomm was offering.

Meanwhile Intel chief Pat Gelsinger is trying its best to save the company. He even sent a memo to his employees in which he said that, “There has been no shortage of rumors and speculation about the company.” 

Coming back to the roots

To compete with NVIDIA and capitalise on the generative AI wave, Intel began manufacturing GPUs, however, unlike NVIDIA, it has struggled to gain market share. According to a recent report, NVIDIA holds 88% of the GPU market share, while Intel is struggling with a minuscule share.

Meanwhile, Intel is all set to launch the Gaudi 3 accelerator today, and it will be interesting to see how the market responds to it. Indian AI companies are among the primary customers of Intel GPUs, as they are comparatively more affordable than those from NVIDIA. Notable Indian customers of Intel include Ola, Krutrim, Zoho, Infosys and CtrlS.

In 2025, the successor to Gaudi 3, Falcon Shores, will merge the AI capabilities of Gaudi with the powerful GPUs from Intel, all within a single package. Intel also plans to onboard another version of the AI accelerator superchip, Falcon Shores 2, by 2026, which will be based on the Gaudi 3 architecture.

However, Intel could be shifting its focus away from GPUs this year. To turn around the fortunes of the company, Intel’s chief urged the team to build on the momentum in the Foundry business as they approach the launch of Intel 18A. He also called for urgency in creating a more competitive cost structure, with the goal of reaching the $10 billion savings target outlined last month.

Alongside these efforts, he stressed the importance of refocusing on Intel’s core x86 business while advancing the company’s AI strategy and simplifying the product portfolio to better meet the needs of customers and partners.

Moreover, Intel recently announced a partnership with Amazon Web Services (AWS) that includes co-investing in custom chip designs. This collaboration features a multi-year, multi-billion-dollar framework covering products and wafers from Intel.

As part of this agreement, Intel Foundry will create an AI fabric chip for AWS using the Intel 18A process. Intel will also produce a custom Xeon 6 chip on Intel 3, continuing its existing partnership that involves manufacturing Xeon Scalable processors for AWS. Looking ahead, Intel expects to have a strong collaboration with AWS on further designs across Intel 18A, Intel 18AP, and Intel 14A.

Recently, Intel has been awarded up to $3B in direct funding under the CHIPS and Science Act for the U.S. government’s Secure Enclave program. Moreover, Intel plans to create Intel Foundry as an independent subsidiary to strengthen its progress in the semiconductor market.

Intel Think ‘Outside’ the Box 

It’s high time for Intel to pivot from being just an AI PC processor company to providing inference solutions as well. Currently, alongside NVIDIA, companies like SambaNova, Cerebras, and Groq are preferred by customers for their ability to deliver exceptionally fast inference solutions. 

This may require a complete architectural change, which might initially seem difficult for Intel, but it is worth trying, otherwise, it risks becoming a sinking ship. 

Not to forget, Intel last year launched 5th Gen Xeon processors, featuring AI acceleration in every core. These processors provide greater performance to customers deploying AI capabilities across cloud, network, and edge use cases.

Nokia is another good example for Intel which pivoted recently. The phone maker that once ruled the mobile market, “connecting people” for over two decades, is now planning to do the same with its networking solutions in the age of AI. 

Recently, Nokia CEO Pekka Lundmark highlighted the company’s unique position in the global market, emphasising that Nokia is the only firm capable of delivering all key networking components outside of China. 

Only time will tell what destiny lies ahead for the company. History shows that the most successful companies are those that have effectively pivoted to meet changing demand. 

In the last few years, apart from the data centre and AI race, Intel seems to be falling behind in the thin and light premium laptop market, which is ruled by Apple Sillion, Qualcomm, and even AMD’s recent launch of Zen Strix Point CPU, which also made headlines for using an ARM chipset.

Intel is not sitting ducks. This time, the company has decided to hit hard with what they are known for as the ‘x86’ platform on its Lunar Lake processors, which not only competes with ARM head-to-head but also provides compatibility of the x86 platform, which is a huge pro of this platform compared to ARM. 

“x86 VS ARM discussion is done,” said Manini Sharma, product marketing manager at Intel, while explaining how Lunar Lake CPUs are on par or even better in some benchmarks compared to Qualcomm’s ARM chipsets. 

A few months back, Qualcomm announced its Snapdragon X Elite chips for laptops. While specific sales figures for these laptops are not yet available, Copilot+ AI PCs (which include X Elite-powered devices) reportedly accounted for 20% of global PC sales during their launch period. 

This is because when Microsoft announced its Copilot+ AI PCs, the requirements seemed to be actively showing love for Qualcomm processors, more than Intel and AMD for its AI PCs, as the capabilities of the current generation of both companies were not even close to what Qualcomm was offering. 

Qualcomm is aiming for ambitious growth, targeting a 50% market share in the PC market by 2029.

But now, with Intel’s Lunar Lake making the move, it seems like Intel will usher in the era of AI PCs with Microsoft. 

What’s so Good About Lunar Lake?

Intel’s CPU and NPU plans are still seemingly strong, along with a focus on edge use cases and on-device AI. Since Intel is currently the majority holder of the laptop industry, with the future of AI racing towards smaller models, it is possible that Intel might rise in a year or two as the leader, spearheading the AI PC game. 

Intel had revealed earlier that starting from the third quarter of 2024, its highly anticipated client processors, codenamed Lunar Lake, are slated to power over 80 fresh laptop designs across more than 20 OEMs. 

Lunar Lake is boasting over three times the AI performance of its predecessors. With an impressive 40+ NPU TOPS, Intel’s next-gen processors are poised to deliver the capabilities required for the upcoming Copilot+ experiences. 

Moreover, Lunar Lake features over 60 GPU TOPS, amounting to more than 100 platform TOPS in total. It is the first product from Intel that comes with integrated memory like Apple Silicon for faster and more efficient data transfer. 

At the launch of Lunar Lake processors, Intel’s senior vice president and general manager, Jim Johnson, revealed that Lunar Lake has the fastest on-board GPU, but when we talk about the AI part, it 120 TOPS (trillion operations per second) of total AI performance across the CPU, GPU, and NPU with double of AI throughput compared to last generation. 

Intel anticipates shipping 40 million AI PCs in 2024, featuring over 230 designs spanning from ultra-thin PCs to handheld gaming devices. There are no PCs without Intel – that’s for sure.

AI + Efficiency, x86 is a Bonus

“Before you can even experience performance, you need your application to just run, and there is nothing more frustrating than loading your app and it not working at all. With Lunar Lake, you don’t need a website to check if your favourite app will run, and we have brought the greatest strength of PC (compatibility) with Lunar Lake,” said Jim Johnson, suggesting that x86 offers much better compatibility compared to ARM processors.

Efficiency is a key part here as Intel is betting on their E cores (efficiency cores) like never before and claiming that they will take care of most of your workloads without using available P cores. 

To showcase Lunar Lake’s AI capabilities, Intel made an extension for VLC which allows users to find objects from the entire video, running locally. It can also be used to find specific videos from a very large library of videos. 

Intel went one step further and showcased how these chipsets can transform verbal descriptions into visual scenes for tabletop role-playing games. It uses speech recognition, language processing, and image generation, to create immersive visual experiences based on a game master’s narration.

Furthermore, Intel also launched a second iteration of AI Playground, a tool which lets you use LLMs locally of your choice, including text-to-image models, to allow normal users a taste of what AI feels like, which is a huge win for the AI PC market.

AI Developers Might Be a Little Disappointed 

Out of all the features and efficiency talks, there’s one thing that might disappoint every AI developer, and that is RAM limitation. As mentioned earlier, RAM is now an integral part of the processor, and you can not upgrade after purchasing. 

The issue is the top notch variant of Lunar Lake only comes with 32 Gigabytes of RAM and can not be upgraded or increased at the time of purchase. So running a model with more than 20B parameters will be impossible on this. 

A Reddit user mentioned that this laptop is meant for normal users, not AI developers. “Despite what you may think, the number of people running LLMs or Flux locally who don’t actually require it for their workload are very small compared to the total install base of Windows,” suggesting the product was targeted to the mass user base but not the niche of AI developers.

But Intel’s Lunar Lake seems to be hitting a decent chunk of the AI PC market with its focus on consumers for now, and not the developers, which might change with the launch of Arrow Lake.

Samsung has appointed Lin Jun-cheng, who worked at its foundry business rival TSMC, who will serve as a senior vice president of the advanced packaging team under Samsung’s chip business division, device solutions. 

Read more: What Does TSMC’s $40 Billion Investment Mean for Chipmakers?

Jun-cheng, who was in TSMC for 19 years, played a significant role in the development of 3D packaging technology at the Taiwan-based chip-making giant. Before joining Samsung, he served as the chief of Skytech, a semiconductor equipment firm in Taiwan. Prior to his tenure at TSMC, Lin worked for US-based Micron Technology which specialises in memory semiconductors.

Lin’s recruitment by Samsung coincides with the company’s strong investment in advanced packaging technology, an area where it has lagged behind global competitors like TSMC and Intel.

Samsung’s High Stakes Bet on Talent Falters as Chip Market Takes a Hit

Kim Woo-pyeong, who previously worked at Apple, was appointed as the head of Samsung’s Packaging Solution Center at device solution last year. In addition, Samsung recruited Benny Katibian, a self-driving chip expert who previously worked for Qualcomm, to improve its self-driving technology. Samsung Research also recently hired Kwon Jung-hyun, a former Nvidia engineer, for robotics research. 

However, Samsung and SK Hynix, are facing difficult situations due to declining memory prices and tighter US restrictions on China. In February, South Korea’s exports fell 75%  to $50.1 billion, with semiconductor exports dropping by 42.5%. South Korea’s exports to China also fell by 24.2% due to weak demand for chips and petrochemical products. 

Samsung is expected to incur an operating loss of up to KRW 4 trillion from its memory chip business in the first quarter of 2023, the first operating loss since the global financial crisis in 2008. The device solutions division, which includes memory, IC, and foundry businesses, accounts for more than half of Samsung’s operating income. In addition, the US-China tension is a significant geopolitical risk for chipmakers, and South Korea’s chip inventory levels have increased by 28% in January compared to the previous month.

Read more: India: A Dumping Ground for Global Semiconductor Waste?

The global data center semiconductor and component market skyrocketed an unprecedented 152 percent in the first quarter of 2024, marking a new milestone, according to a report from Dell’Oro Group.

This explosive growth was fueled by insatiable demand for GPUs and custom accelerators, particularly in the hyperscale cloud sector.

The report revealed that in Q1 2024, NVIDIA led all vendors in component revenues, accounting for nearly half of the reported figures, as supplies of its H100 GPUs improved for both cloud and enterprise markets. Samsung and Intel followed NVIDIA in the rankings.

Looking ahead, strong growth for accelerators is expected to continue into 2024, with GPUs remaining the primary choice for AI training and inference workloads. NVIDIA’s upcoming Blackwell platform is poised to strengthen the firm’s leadership position.

However, the report anticipates that custom accelerators and offerings from other vendors, such as the AMD MI300X/MI325X Instinct and Intel Gaudi3, will gain some market share.

The report also noted that revenues for Smart NICs and DPUs surged more than 50 percent in Q1 2024, driven by strong hyperscale adoption for both AI and non-AI use cases. Storage drives and memory saw significant price increases as vendors aimed to align supply with demand. The three major memory suppliers shifted production capacity from DRAM to AI-focused High Bandwidth Memory (HBM) products.

Baron Fung, Senior Research Director at Dell’Oro Group, highlighted, “Accelerators such as GPUs continue to drive substantial growth, with shipments hitting record highs each quarter. Meanwhile, traditional server and storage component markets returned to positive year-over-year growth as vendors and cloud service providers ramped up purchases in anticipation of robust system demand later this year.”

General-purpose computing components also rebounded strongly following an inventory correction cycle in 2023, experiencing double-digit revenue growth. “Average selling price (ASP) of components has increased significantly from a year ago adding to topline growth,” Fung explained.

“For CPUs, an increasing mix toward fourth- and fifth-generation CPUs, which have more cores and feature sets compared to their predecessors, have commanded higher ASPs.”

As data centers continue to expand and evolve to support the explosive growth of AI and cloud computing, the demand for high-performance semiconductors and components shows no signs of slowing down.

At Computex 2024, Intel has officially announced details about its forthcoming Lunar Lake chips, set to power Copilot+ AI PCs this fall. These new chips will deliver up to 48 TOPS (tera operations per second) of AI performance, supported by an upgraded neural processing unit (NPU). 

This represents a significant leap from Intel’s previous Meteor Lake chips, which offered a 10 TOPS NPU, and positions Intel in the ongoing AI performance race against competitors like AMD and Qualcomm.

Unveiled at Computex, Intel’s Lunar Lake chips promise substantial advancements. Alongside the impressive AI performance, they will feature a new Xe2 GPU, providing 80 percent faster gaming performance compared to the previous generation. 

Additionally, an AI accelerator in the chip will contribute an extra 67 TOPS of performance. Despite these enhancements, Intel faces competition from AMD’s Ryzen AI 300 chips, launching in July with 50 TOPS NPUs, and Qualcomm’s Snapdragon X Elite and X Plus chips. These competitors highlight the aggressive push within the AI PC market. 

In a notable development, Lunar Lake chips will include on-board memory, akin to Apple Silicon. Options of 16GB or 32GB of RAM will be available, but like Apple’s design, these will not be upgradable. 

This integration allows for a reduction in latency and a 40 percent decrease in system power usage, although it limits users needing more RAM until Intel’s next chip family, Arrow Lake, is released.

Lunar Lake will also feature eight cores, split between performance and efficiency (P-cores and E-cores). The chip includes an “advanced low-power island” for efficiently managing background tasks, contributing to a claimed 60 percent improvement in battery life over Meteor Lake.

Despite these enhancements, Intel faces competition from AMD’s Ryzen AI 300 chips, launching in July with 50 TOPS NPUs, and Qualcomm’s Snapdragon X Elite and X Plus chips. These competitors highlight the aggressive push within the AI PC market. 

Qualcomm’s chips, known for their power efficiency, reportedly achieve over 20 hours of battery life on Copilot+ Surface devices, although independent testing is pending.

Connectivity for Lunar Lake will include Wi-Fi 7, Bluetooth 5.4, PCIe Gen5, and Thunderbolt 4. However, Intel has not yet committed to integrating Thunderbolt 5, which is expected to launch later this year.

During a media briefing ahead of Computex, Intel shared benchmark results, indicating Lunar Lake’s superiority over Meteor Lake in tasks like running Stable Diffusion. Lunar Lake completed 20 iterations in 5.8 seconds, compared to 20.9 seconds for Meteor Lake, despite drawing slightly more power.

Specific chip models and deeper specifications for Lunar Lake are yet to be disclosed, but Intel’s latest offerings mark a significant stride in AI and PC performance, setting high expectations for their launch this fall.

In a move to address the evolving needs of small and medium-sized enterprises (SMEs) and hosted IT service providers, AMD has introduced the AMD EPYC 4004 Series processors. 

These new offerings, announced today, complement AMD’s existing EPYC server CPU lineup, providing cost-optimised solutions without compromising on performance and enterprise-class features.

Powered by the efficient “Zen 4” architecture, the AMD EPYC 4004 Series CPUs offer enterprise-grade performance, scalability, and modern security features, catering to price-conscious buyers. 

Notably, a server equipped with a single AMD EPYC 4564P CPU outperforms an Intel Xeon E-2488 CPU by 1.8 times in terms of performance per dollar.

John Morris, corporate vice president of the Enterprise and HPC Business Group at AMD, emphasised the significance of these processors for businesses that historically had to settle for IT solutions that didn’t fully meet their requirements. “Based on the same technologies that power the most demanding data centres in the world, the AMD EPYC 4004 Series processors are offered at an optimised acquisition cost for customers in small and medium-sized businesses seeking to drive better business outcomes,” he said.

The AMD EPYC 4004 Series processors are engineered to deliver robust, general-purpose computing in a single-socket package, facilitating highly performant rack scale, multi-node, and tower configurations, particularly suitable for scenarios where system cost and infrastructure constraints are crucial considerations.

Key industry players have expressed their support for AMD’s initiative. Kamran Amini, Vice President and General Manager for Server, Storage & Software Defined Solutions at Lenovo, praised AMD’s efforts in expanding its EPYC processor roadmap to address a broader market segment with affordable yet high-performance capabilities.

OVHcloud’s chief product and technology officer, Yaniv Fdida, echoed the sentiment, expressing enthusiasm about adding AMD EPYC 4004 CPU-powered solutions to their Bare Metal portfolio, emphasising the potential for flexibility and performance-price ratio benefits in data centers.

Supermicro’s SVP Marketing and Network Security, Michael McNerney, highlighted the enhanced value brought by AMD EPYC 4004 Series CPUs to customers seeking cost-effective and easy-to-deploy solutions, particularly in workload performance optimization for hosting, content delivery, and cloud workloads.

Overall, the AMD EPYC 4004 CPU-powered servers promise a compelling balance of performance, scalability, and affordability, catering to a wide range of enterprise solutions. Supported by leading partners such as Altos, ASRock Rack, Gigabyte, MSI, New Egg, Tyan, and others, these processors signify AMD’s commitment to meeting the diverse needs of growing businesses.

Intel has revealed that starting from the third quarter of 2024, its highly anticipated client processors, codenamed Lunar Lake, are slated to power over 80 fresh laptop designs across more than 20 original equipment manufacturers (OEMs). 

These processors are primed to usher in a new era of AI performance on a global scale for Copilot+ PCs, set forward by Microsoft.

Underlining the significance of this development, Michelle Johnston Holthaus, Executive Vice President and General Manager of the Client Computing Group at Intel, emphasised the breakthrough power efficiency and compatibility of the x86 architecture. “With breakthrough power efficiency, the trusted compatibility of x86 architecture and the industry’s deepest catalogue of software enablement across the CPU, GPU and NPU, we will deliver the most competitive joint client hardware and software offering in our history with Lunar Lake and Copilot+,” he said.

An AI PC, comprising a CPU, GPU, and NPU, is tailored with specific AI acceleration capabilities. The NPU, in particular, serves as a specialised accelerator for AI and machine learning tasks directly on the PC, bypassing the need for cloud processing. The rising importance of AI PCs stems from the growing necessity to automate and optimise tasks on personal computers.

Lunar Lake is anticipated to revolutionise mobile processing for AI PCs, boasting over three times the AI performance compared to its predecessors. With an impressive 40+ NPU tera operations per second (TOPS), Intel’s next-gen processors are poised to deliver the capabilities required for the upcoming Copilot+ experiences. Moreover, Lunar Lake will feature over 60 GPU TOPS, amounting to more than 100 platform TOPS.

“The launch of Lunar Lake will bring meaningful fundamental improvements across security, battery life, and more thanks to our deep co-engineering partnership with Intel. We are excited to see Lunar Lake come to market with a 40+ TOPS NPU which will deliver Microsoft’s Copilot+ experiences at scale when available,” said Pavan Davuluri, Corporate Vice President of Windows + Devices at Microsoft.

Recognising the importance of both hardware innovation and software enablement, Intel is actively collaborating with over 100 independent software vendors through its AI PC Acceleration Program. This initiative aims to enhance AI PC experiences across various domains, including personal assistants, audio effects, content creation, gaming, security, streaming, and video collaboration.

According to reports, AMD is also coming up with its new APU, Ryzen 8050, featuring Zen 5 CPU and XDNA 2 NPU architecture for AI PC workloads. This will boast a performance of around 50 TOPS, ideal for running Microsoft’s AI PC goals.

According to a recent report by IDC, unveiled at Intel’s AI for India Conference in Delhi, India’s spending on AI may reach $5.1 billion by 2027. This surge is attributed largely to AI infrastructure provisioning. This includes spending on hardware such as servers and chips, as well as software components like frameworks and libraries.

Santhosh Viswanathan, vice president and managing director, India region, Intel, said, “With an unmatched talent pool, frugal innovation, and data at scale, India stands poised to lead the global AI revolution.” He added that when it comes to building AI capabilities within India, the country does not necessarily need to rely on big GPUs.

Viswanathan said that when it comes to most of the solutions being built in India, Intel’s Xeon processors are enough to deliver the AI needs. “If you are an enterprise running a model with say 15 to 30 billion parameters, Xeon is enough to run these models effectively,” he said. 

Viswanathan also highlighted that if companies are building models for RAG on personal data inference, Xeon becomes a powerhouse. “If you have small datasets that are very local and do not have many parameters, Xeon is available everywhere for you to test and try out,” he added, saying that customers can already test out the current models available in the market on the existing Xeon-powered data centres across the country.

Xeon is omnipresent

“Not everybody is trying to build the next largest LLM and needs a trillion parameters,” said Viswanathan. Another use case that he highlights is on-edge, for which Intel’s CPU and NPU are very well positioned for privacy and the cost is significantly lower too. 

“AI is not everywhere yet, it’s in one place and you need a lot of GPUs and massive data centres [for building AI]. But over time this is going to change and the costs will come down,” he added.

“You do not need to go back and build massive infrastructures. AI can start today with the infrastructure that you have,” he said. Viswanathan explained that Intel’s go-to-market strategy is about making customers in India realise that they can existing infrastructure that is already using Xeon processors.

Viswanathan said that the reason Intel is going bullish on India is the country’s ability to solve big problems with frugality, like in the case of UPI. He narrated how Intel was the company to bring WiFi in India and just like the internet, Viswanathan said, Intel wants to bring AI everywhere in India. 

“Intel’s goal is to democratise access, and the architecture is open,” said Viswanathan. He added that today, people are waiting for compute and this is where Intel comes in with its Xeon processors. Apart from running high-end AI models, Xeon is also effective and scalable for other workloads, and does not cost as much. 

“That is why I am bullish on Xeon as it is already available across all databases. It is omnipresent,” he added.

Intel also offers its Developer Cloud where customers can test out its offerings while running them in a secure environment. 

For Intel, AI stands for ‘Amazing India’

“When you really need to build something big and test the performance, Gaudi is always there,” Viswanathan said, and added that the company is working with several partners in India to test and benchmark its AI hardware. All of this is along with making AI PCs in partnership with OEM ecosystems such as HPE, Dell, and Lenovo. 

Furthermore, the recently announced Gaudi 3 at Intel Vision accelerator is expected to outperform the NVIDIA H100 by 50% in inference throughput on an average and achieve a 40% increase in inference power-efficiency across different parameter models. 

This, along with the newer Xeon 6 processor, are also optimised heavily for RAG.

Intel is positioning itself in the market as a low-cost alternative to its competitors like NVIDIA and AMD. Viswanathan said that Intel is always an alternative for a company that is struggling with acquiring compute as the cost is too high. He explains that Xeon is a workhorse for a lot of use cases that do not need an accelerator.

Intel indeed has been bullish on India. There were several collaborations announced at the Intel Vision 2024 such as Bharti Airtel, Infosys, and Ola Krutrim. Moreover, Zoho is also leveraging Intel’s processors for its generative AI offerings.

Infosys’ partnership with Intel is about integrating 4th and 5th Gen Intel Xeon processors, Intel Gaudi 2 AI accelerators, and Intel Core Ultra into Infosys Topaz. This collaboration aims to offer AI-first services, solutions, and platforms to accelerate business value through generative AI technologies. 

Ola Krutrim recently launched its open-source model on Databricks platforms. The company utilised Intel Gaudi 2 clusters to pre-train and fine-tune its foundational models with generative capabilities in ten languages, achieving industry-leading price/performance ratios compared to existing market solutions. 

Additionally, Krutrim is currently pre-training a larger foundational model on an Intel Gaudi 2 cluster, further advancing its AI capabilities.

Intel also has Make in India partners and is in talks with the government to build systems locally and fully designed in India. “Anybody who is keen on reducing the carbon footprint while also reducing the cost on their wallet, we are absolutely there,” added Viswanathan.

Intel has unveiled the world’s largest neuromorphic system, named Hala Point, to promote more sustainable and efficient AI. Utilising Intel’s Loihi 2 processor, the system is designed for research in brain-inspired AI and addresses challenges in today’s AI efficiency and sustainability. 

Hala Point, initially deployed at Sandia National Laboratories, improves on Intel’s previous large-scale research system, Pohoiki Springs, with over 10 times more neuron capacity and up to 12 times higher performance.

“The computing cost of today’s AI models is rising at unsustainable rates. The industry needs fundamentally new approaches capable of scaling. For that reason, we developed Hala Point, which combines deep learning efficiency with novel brain-inspired learning and optimisation capabilities. We hope that research with Hala Point will advance the efficiency and adaptability of large-scale AI technology,” said Mike Davies, director of the Neuromorphic Computing Lab at Intel Labs.

Hala point can support up to 20 quadrillion operations per second, or 20 petaops, with an efficiency exceeding 15 trillion 8-bit operations per second per watt (TOPS/W) when executing conventional deep neural networks.

These capabilities surpass those of systems based on GPUs and CPUs. Its advanced features enable real-time continuous learning for applications such as smart city management, scientific problem-solving, and large language models.

Sandia National Laboratories plans to use Hala Point for advanced brain-scale computing research, focusing on scientific computing challenges across various disciplines. The system’s large-scale capacity allows researchers to tackle complex problems in fields ranging from commercial to defence to basic science.

“Working with Hala Point improves our Sandia team’s capability to solve computational and scientific modelling problems. Conducting research with a system of this size will allow us to keep pace with AI’s evolution in fields ranging from commercial to defence to basic science,” said Craig Vineyard, Hala Point Team Lead, Sandia National Laboratories.

While Hala Point is a research prototype, its development promises advancements such as continuous learning in large language models, significantly reducing the training burden in AI deployments. Intel anticipates further progress in the field by applying neuroscience-inspired computing principles to minimise power consumption and maximise performance.

Indian companies are very much dedicated to building their own AI models. And Intel has been a long lover when it comes to delivering solutions within the country in every technological domain. Now, Intel is taking another step forward by partnering with Indian companies for delivering its AI hardware. 

“Boy was I startled when I learned that India is very convinced they need their own models for their environment” said Patrick Gelsinger, CEO of Intel at Intel Vision 2024. “They are excited to train and be able to deliver that using Gaudi clusters,” he added. 

Apart from Xeon 6 and Gaudi 3, there were various new collaborations announced at the event, many within India. 

The buzzing partner ecosystem

Bharti Airtel aims to harness its extensive telecom data to enhance AI capabilities, thereby enriching customer experiences and exploring new revenue avenues in the digital realm.

Infosys has announced a strategic partnership with Intel, integrating Intel technologies such as 4th and 5th Gen Intel Xeon processors, Intel Gaudi 2 AI accelerators, and Intel Core Ultra into Infosys Topaz. This collaboration aims to offer AI-first services, solutions, and platforms to accelerate business value through generative AI technologies.

Infosys also plans to utilise Intel’s AI training resources to educate its employees about Intel’s product offerings, enabling them to offer generative AI expertise to the company’s extensive international customer base across various industries.

Ola Krutrim is utilising Intel Gaudi 2 clusters to pre-train and fine-tune its foundational models with generative capabilities in ten languages, achieving industry-leading price/performance ratios compared to existing market solutions. Additionally, Krutrim is currently pre-training a larger foundational model on an Intel Gaudi 2 cluster, further advancing its AI capabilities.

CtrlS, one of the largest and fastest growing data centres in the world, which is hosting most of the providers in India is also using Gaudi 2 and Xeon processors, revealed Gelsinger in his keynote. 

In March, L&T had also announced its collaboration with Intel for deploying scalable edge-AI solutions across various domains, including Cellular Vehicle-to-Everything (CV2X) applications, leveraging the expertise in connected vehicles and smart transportation systems alongside Intel’s Edge Platform.

Just this month, Zoho also collaborated with Intel for optimising AI workloads within the company. Santosh Viswanathan, vice president and managing director at Intel, said that Zoho has witnessed significant performance improvements in AI workloads with 4th Gen Intel Xeon processors.

Though most of these partnerships come with the last generation of Gaudi and Xeon, the leadership has been quite vocal about the expansion plans within the country.

India is set as a distinct entity

Intel is betting big on India, which is not that new. Providing cheaper alternatives when it comes to data centres and powering enterprise solutions, Intel has always been the go to choice for Indian companies. While NVIDIA is increasingly expanding its partnership within India with entities like Yotta for establishing data centres, Intel remains a viable option for already established customers within India. 

“AI does not just require big GPUs to solve the problem. There are a lot of different models that can run on Xeon. Innovation at scale can happen with Xeon. We are working with several large customers. Gaudi 2 is available, Gaudi 3 comes in the second half. You will see some of those products coming into India through these customers as well,” Viswanathan said earlier.

Christoph Schell, executive vice president of Intel said that the company is betting big on India when it comes to AI by carving it out as a separate geographic region. The American chip manufacturer is introducing a new era of computing with the release of its AI-powered PC in late 2023. 

These systems, featuring Intel’s Core Ultra processors tailored for AI tasks, improve user productivity and experience. Intel’s AI PCs are currently available in the market, and numerous retailers have started distributing them in India. By 2025, Intel aims to supply core processors for as many as 100 million AI-enabled PCs, much of which would be through India. 

Though Viswanathan has said that the company currently has no plans for setting up its fab within the country, it is still betting big on AI in India through other ways. 

“World needs a balanced supply chain. You cannot have 80% of servers being made in one place and 90% of all laptops made in one place. I think that’s the key change where India can really step and help build a balanced electronics supply chain for the world,” he said.

Viswanathan said India has about 20% of the world’s data sets that can be used for AI models training.

“We are very frugal. 16 or 20% of the world’s AI talent is in India. We kind of lead the world and not follow in this path. That’s another piece that makes me bullish about India. For me India is most exciting. AI is not just artificial intelligence, it is also amazing in India. No other country has digital infrastructure at the scale that we have. India stack is a game changer,” Viswanathan said.

At the Intel Vision 2024, the company has introduced its latest innovation in the realm of data centre, cloud, and edge computing with the launch of the new Intel Xeon 6 processors. 

Designed to offer performance-efficient solutions for running AI applications such as RAG, these processors aim to deliver business-specific results by leveraging proprietary data. The new brand, Intel Xeon 6, heralds a significant leap in processing power and efficiency, catering to the evolving needs of modern computing landscapes.

The current ‘Emerald Rapids’ Fifth-Gen Xeon models from Intel will not undergo a rebranding, indicating that the new branding scheme will exclusively pertain to Xeon 6 and subsequent processor iterations.

Under the hood, the Intel Xeon 6 processors boast two distinct variants: those equipped with Efficient-cores (E-cores) and those featuring Performance-cores (P-cores). 

The E-core processors, codenamed Sierra Forest, promise a remarkable 2.4x improvement in performance per watt and a staggering 2.7x enhancement in rack density compared to their predecessors, the 2nd Gen Intel Xeon processors. This advancement not only amplifies computational capabilities but also enables customers to replace outdated systems at a ratio of nearly 3-to-1, thereby substantially reducing energy consumption and contributing to sustainability goals.

On the other hand, the P-core processors, codenamed Granite Rapids, introduce software support for the MXFP4 data format. This integration results in a notable reduction in next token latency by up to 6.5x compared to the 4th Gen Intel Xeon processors using FP16. Furthermore, with the ability to run 70 billion parameter Llama 2 models, these processors are poised to elevate AI performance to unprecedented heights.

At the Intel AI Everywhere event in December, Intel had revealed the forthcoming release of 5th Gen Xeon processors, featuring AI acceleration in every core and expected to hit the market in 2024. Unveiled by Intel CEO Pat Gelsinger, these processors, previously codenamed Emerald Rapids, mark a significant advancement in computing. 

In addition to advancements in processing power, Intel has also announced significant developments in client, edge, and connectivity solutions. The company’s Intel Core Ultra processors are driving new capabilities for productivity, security, and content creation, presenting an enticing proposition for businesses to refresh their PC fleets. 

Intel anticipates shipping 40 million AI PCs in 2024, featuring over 230 designs spanning from ultra-thin PCs to handheld gaming devices.

Looking ahead, Intel’s roadmap includes the launch of the next-generation Intel Core Ultra client processor family, codenamed Lunar Lake, in 2024. This lineup is projected to deliver more than 100 platform tera operations per second (TOPS) and over 45 neural processing unit (NPU) TOPS, ushering in a new era of AI-centric computing.

Furthermore, Intel has unveiled new edge silicon across its product families, targeting key markets such as retail, industrial manufacturing, and healthcare. These additions to Intel’s edge AI portfolio are slated for availability this quarter and will be supported by the Intel Tiber Edge Platform throughout the year.

In a bid to revolutionise Ethernet networking for AI fabrics, Intel is spearheading the Ultra Ethernet Consortium (UEC), introducing a range of AI-optimised Ethernet solutions. These innovations are designed to cater to the evolving needs of large-scale AI fabrics, enabling seamless training and inferencing for increasingly complex models.

Intel has announced its latest AI chip Gaudi 3 at the Intel Vision 2024 event, in a bid to keep pace with the growing demand for semiconductors capable of training and deploying large AI models.

The newly introduced Gaudi 3 chip, which was revealed by CEO Pat Gelsinger at Intel AI Everywhere event, boasts over double the power efficiency compared to its predecessor and is capable of running AI models 1.5 times faster than NVIDIA’s H100 GPU. 

It offers various configurations, including a bundle of eight Gaudi 3 chips on one motherboard or a card that can be integrated into existing systems.

Gaudi 3, built on a 5 nm process, signals Intel’s utilisation of advanced manufacturing techniques. Additionally, Intel plans to manufacture AI chips, potentially for external companies, at a new Ohio factory expected to open in the coming years, according to Gelsinger.

During testing, Intel evaluated the chip’s performance on models like Meta’s open-source Llama and the Falcon model by TII. Gaudi 3 demonstrated its capability to facilitate the training or deployment of models such as Stable Diffusion or OpenAI’s Whisper model for speech recognition.

Compared to the NVIDIA H100, Intel anticipates that Gaudi 3 will achieve an approximately 50% faster time-to-train on average across Llama 2 models with 7B and 13B parameters, as well as the GPT-3 175B parameter model. 

While performance data for NVIDIA’s recently announced Blackwell-based B200 Tensor GPU is not currently available, it’s clear that NVIDIA’s latest offering would likely affect these performance comparisons significantly.

In comparison to NVIDIA, Intel claims its chips consume less power. NVIDIA currently dominates approximately 80% of the AI chip market with its GPUs, which have been the preferred choice for AI developers in the past year.

Intel asserts that its Gaudi 3 AI accelerator offers an estimated 50% enhancement in inferencing performance and around 40% better power efficiency compared to NVIDIA’s H100. Moreover, Intel states that it achieves these benefits at a significantly lower cost.

Intel has announced that Gaudi 3 chips will be available to customers in the third quarter, with companies like Dell, HPE, and Supermicro set to incorporate the chips into their systems. However, Intel hasn’t disclosed the pricing details for Gaudi 3.

Das Kamhout, vice president of Xeon software at Intel, expressed confidence in Gaudi 3’s competitiveness against NVIDIA’s latest offerings, citing factors such as competitive pricing and the incorporation of an open integrated network on chip.

The data centre AI market is expected to expand as cloud providers and businesses invest in infrastructure for deploying AI software, indicating opportunities for other players in the market.

While NVIDIA has seen significant stock growth driven by the AI boom, Intel’s stock has experienced more modest gains. Nevertheless, Intel remains determined to compete in the AI chip market, with AMD also seeking to expand its presence in the server AI chip segment.

NVIDIA’s success has largely been attributed to its proprietary software suite, CUDA. In contrast, Intel is collaborating with chip and software giants like Google, Qualcomm, and Arm to develop open software solutions, aiming to provide greater flexibility for software companies in selecting chip providers.

In addition, Intel unveiled its intention to create an open platform for enterprise AI, aiming to expedite the deployment of secure GenAI systems empowered by retrieval augmented generation (RAG).

In a recent blog post titled “Behind the Compute,” Stability AI, unveiled shocking findings regarding the performance of Intel Gaudi 2 accelerators compared to NVIDIA’s H100 in training and inference of its upcoming image generation model Stable Diffusion 3.

Stability AI’s text-to-image model demonstrated promising results in the performance analysis. Utilising the 2B parameter multimodal diffusion transformer (MMDiT) version of the model, Stability AI compared the training speed of Intel Gaudi 2 accelerators with NVIDIA’s A100 and H100.

On 2 nodes configuration, Intel Gaudi 2 system processed 927 training images per second, 1.5 times faster than NVIDIA H100-80GB. Further increasing the batch size to 32 per accelerator in Gaudi 2 resulted in a training rate of 1,254 images/sec.

On 32 Nodes Configuration, the Gaudi 2 cluster processed over 3x more images per second compared to NVIDIA A100-80GB GPUs, despite A100s having a highly optimised software stack.

On inference tests with the Stable Diffusion 3 8B parameter model, Gaudi 2 chips offered similar inference speed to NVIDIA A100 chips using base PyTorch.

However, Stability AI admitted that with TensorRT optimisation, A100 chips produced images 40% faster than Gaudi 2, but Stability AI anticipates Gaudi 2 to outperform A100s with further optimisation. This can be further contented with the upcoming GH200 processors that might be announced at GTC 2024 this month. 

Few months back, AMD also claimed that it has surpassed NVIDIA H100 on various performance metrics, but it was later debunked by NVIDIA as it said that AMD also did not include TensorRT optimisation for the test. 

Intel has also launched its Gaudi 3 AI accelerator which would make this competition even interesting in the future. 

Moreover, Stable Beluga 2.5 70B, Stability AI’s fine-tuned version of LLaMA 2 70B, showcased impressive performance on Intel Gaudi 2 accelerators. Running the PyTorch code out of the box on 256 Gaudi 2 accelerators, Stability AI measured an average throughput of 116,777 tokens/second.

Gaudi 2 demonstrated a 28% faster performance compared to NVIDIA A100 in inference tests with the 70B language model, generating 673 tokens/second per accelerator.

In a significant move to enhance smart city infrastructure and intelligent transportation systems, L&T, a leading global digital engineering and R&D services company, has announced a collaboration with Intel Corporation. 

This partnership is set to develop and deploy scalable edge-AI solutions across various domains, including Cellular Vehicle-to-Everything (CV2X) applications, leveraging LTTS’s expertise in connected vehicles and smart transportation systems alongside Intel’s cutting-edge Edge Platform.

The collaboration aims to harness the power of Intel’s Edge Platform, equipped with built-in AI runtime and OpenVINO inference for real-time AI inferencing optimisation. This technology will enable LTTS to enhance traffic management and emergency safety measures in smart cities and transportation sectors, addressing the critical need for advanced networking and AI analytics at the edge with low latency, locality, and cost-effectiveness.

Abhishek Sinha, Chief Operating Officer and Board Member at L&T Technology Services, expressed enthusiasm about the partnership, stating, “LTTS is delighted to collaborate with Intel on launching their new Edge Platform, which promises to democratise access to edge-AI solutions. With deep-rooted hardware optimisation at its core, our enterprise customers can trust Intel’s Edge Platform to propel them into a future of unparalleled performance and possibilities.”

Intel’s Edge Platform heralded as a game-changer, offers a comprehensive ecosystem with modular building blocks and premium service and support offerings, designed to scale infrastructure across various industries horizontally.

Pallavi Mahajan, Intel Corporate Vice President And General Manager of Network and Edge Group Software highlighted the benefits of this collaboration for industries such as transportation and smart cities. She noted, “The collaboration with LTTS on Intel’s Edge Platform will simplify the exchange of critical information and streamline infrastructure management to improve results and lower TCO for customers.”

This partnership underscores LTTS’s commitment to advancing smart cities and road infrastructure, setting the stage for developing intelligent transportation systems that promise enhanced road safety, accident prevention, and improved mobility for the future.

HCLTech and Intel Foundry have announced their decision to expand their collaboration to co-develop silicon solutions to improve semiconductor innovation globally. This partnership will leverage HCLTech’s design expertise and Intel Foundry’s advanced technology and manufacturing capabilities. 

The goal is to establish a resilient and diversified supply chain to meet the rising global demand for semiconductor manufacturing. This collaboration will offer semiconductor manufacturers, system OEMs, and cloud services providers a robust ecosystem for semiconductor sourcing. Additionally, the collaboration has the potential to spur innovation by enabling the design of customised silicon solutions tailored to specific use cases.

“Intel Foundry’s advanced technologies and silicon-verified IPs in manufacturing and advanced packaging strengthens our delivery of innovative, accessible and diverse solutions to our mutual clients. This will also give them greater choice and flexibility in semiconductor sourcing,” said Vijay Guntur, President, Engineering and R&D Services, HCLTech.D

HCLTech has been collaborating with Intel for over 30 years, a relationship that has evolved through shared offerings and joint investments in various sectors, including silicon services, hardware engineering, telecom services, and more. The current focus is on jointly designing highly customised silicon solutions for companies, combining HCLTech’s design expertise with Intel’s manufacturing capabilities.

This expanded collaboration is set to further strengthen their partnership by fostering a strong and open ecosystem beneficial for clients requiring advanced silicon solutions.

Intel also announced that it has signed Microsoft as a foundry customer for a custom chip. This deal is part of Intel’s plan to overtake TSMC using its Intel 18A and upcoming 14A manufacturing technologies. The 18A, a 1.8nm technology, is set for early 2025 and will be used for future CPUs in both consumer and data centre markets. The 14A, planned for late 2026, will introduce a more advanced lithography tool for smaller and more efficient chips. 

Together with its collaboration with HCLTech to develop customised silicon solutions, Intel aims to meet the growing demand for semiconductors. 

Intel Foundry Services (IFS) is set to host IFS Direct Connect on February 21, 2024, at the San Jose McEnery Convention Center. Sam Altman, Chief Executive of OpenAI, will mark his presence at the event as a luminary speaker.

“Thrilled Sam Altman is joining me at Direct Connect on Feb 21. Sam is a renowned leader on AI & its impact on the world. Looking forward to discussing the role semis play in enabling modern society,” wrote Pat Gelsinger, Intel chief.

“Infinite possibilities ahead in the age of AI & no better convo for Intel’s 1st Foundry event. See you soon, Sam!” he added.”

The dialogue is expected to delve into the critical role played by semiconductors in shaping modern society, exploring the limitless possibilities in the age of AI.The IFS Direct Connect event promises exclusive insights from Intel executives and ecosystem partners. 

Attendees can anticipate learning about Intel’s strategies, process technology, advanced packaging, and ecosystem collaborations. Of particular interest will be the discussion on how Intel Foundry Services can empower silicon designs, leveraging Intel’s resilient, secure, and sustainable supply chain.

Recently, reports surfaced that Altman is planning to raise billions for an AI chip venture aimed at developing a ‘network of factories’ for fabrication that would stretch around the globe, involving collaboration with unnamed ‘top chip manufacturers.’ 

Last week, Altman visited South Korea to meet with executives from Samsung Electronics and SK Hynix, exploring the potential for an alliance in the field of AI chips. Other reports suggest that Altman is in discussions with Middle Eastern investors and chip fabricators, including TSMC, about launching a new chip venture.

Recently, Intel researchers unveiled a new LLM inference solution with low latency and high throughput for Intel GPUs. They showed that their solution achieved up to 7x lower latency and up to 27x higher throughput than standard HuggingFace implementation. 

As LLMs continue to play a pivotal role across various industries, optimising their performance has become a critical focus, and Intel’s latest development promises to be a game-changer. Tackling the inherent complexity of LLMs, characterised by intricate model structures and autoregressive inference modes, the team behind this breakthrough presents an efficient alternative.

One of the primary challenges the research team addresses is the intricate design of LLMs, characterised by intricate model structures and extensive autoregressive operations. The complexity leads to massive memory access and hampers inference speed.

A simplified LLM decoder layer is at the heart of their solution, strategically designed to fuse data movement and element-wise operations. This fusion reduces memory access frequency and significantly lowers system latency, paving the way for faster and more efficient inference processes.

Read: What is Intel’s AI Plan for 2024

How is Intel pushing the boundaries?

Intel’s solution begins with a streamlined approach to the LLM decoder layer. The team successfully reduces memory access frequency by fusing data movement and element-wise operations, substantially lowering system latency.

Another key innovation is introducing a segment KV (key/value) cache policy. This strategic separation of key and value elements for request and response tokens in distinct physical memory segments proves instrumental in effective device memory management. The outcome is an expanded runtime batch size and improved overall system throughput.

The team customises a Scaled-Dot-Product-Attention kernel to complement their innovative approach, aligning it seamlessly with their fusion policy based on the segment KV cache solution. The result is a finely tuned LLM inference solution that promises to reshape the efficiency standards for these powerful language models.

The research team has not only conceptualised these innovations but has also translated them into a practical solution. Their LLM inference solution is implemented on Intel GPUs and is now publicly available for scrutiny and use.

The substantial reduction in token latency enhances system responsiveness, making it an ideal fit for applications where quick processing is crucial. Simultaneously, the significant boost in throughput facilitates the swift execution of larger tasks, making this solution particularly attractive for real-world, high-demand scenarios.