The DeepSeek effect is real. On Monday, NVIDIA’s stock saw a sharp decline of about 17%, ending the day at roughly $118.58. This plunge wiped out nearly $600 billion in market value, setting a new record for the largest single-day loss in market capitalisation for any company on Wall Street.
The market has attributed the decline to DeepSeek, which recently released its latest model, DeepSeek-R1, trained using NVIDIA’s lower-capability H800 processor chips with a budget of under $6 million.
However, the tech industry may be misinterpreting the situation as DeepSeek-R1 was also trained on NVIDIA GPUs.
Currently, the only potential concern for NVIDIA is that the compute power and cost required to develop next-generation models might decrease in the near future. However, this would be positive for the industry, as demand for these models increases over time, and more consumers will adopt them.
After its shares plunged and DeepSeek became the talk of the town, NVIDIA released a statement saying that its chips are proving valuable in the Chinese market and more will be needed to support DeepSeek’s growing demand.
“DeepSeek’s work illustrates how new models can be created using that technique, leveraging widely available models and compute that is fully export control compliant,” the company said.
The company added that inference requires a large number of NVIDIA GPUs and high-performance networking, mentioning that there are now three scaling laws, pre-training, post-training, and the new test-time scaling.
During a recent interview, the CEO of Scale AI, Alexandr Wang, said that he believes DeepSeek possesses around 50,000 NVIDIA H100s, though they are not permitted to talk about it.
Notably, the company recently launched Janus-Pro-7B, an open-source multimodal AI model created to challenge industry leaders like OpenAI’s DALL-E 3 and Stability AI’s Stable Diffusion in text-to-image generation.
Is DeepSeek the Real Cause of the Market Crash?
Before the release of DeepSeek-R1, the AI research lab launched DeepSeek V3, which, according to the company, was trained on a cluster of 2,048 NVIDIA H800 GPUs with a budget of only $5.576 million.
“Wow… NVIDIA dropped by 17% because of DeepSeek. I wonder if the investors realise that NVIDIA and DeepSeek aren’t competition. DeepSeek was trained using NVIDIA GPUs… People installing and running it locally are mostly using NVIDIA GPUs too…,” said Matt Wolfe, founder of Future Tools.
Similarly, Aashay Sachdeva, an engineer at Sarvam AI, expressed confusion over why NVIDIA is losing money due to DeepSeek. “RL is so behind, they also need even more GPUs to train it with more data for longer periods,” he wrote in a post on X.
He went on to say that DeepSeek’s base model is open-source, which will likely lead to more smaller labs participating, adding that more high-end GPUs will be required for inference. “100k output tokens in open-source models are coming soon,” he added.
The sentiment was echoed by Microsoft CEO Satya Nadella, who remarked, “Jevons paradox is coming again! As AI gets more efficient and accessible, we will see its use skyrocket, turning it into a commodity we just can’t get enough of.”
Jevons paradox suggests that when technology improves and allows us to use a resource more efficiently (for example, using less coal to produce the same amount of energy), the cost of using that resource decreases. This, in turn, can make the resource more attractive and lead to an increase in demand for its use, even though it’s more efficient.
Likewise, OpenAI chief Sam Altman finally took notice of DeepSeek-R1 and said, “DeepSeek’s R1 is an impressive model, particularly in terms of what they’re able to deliver for the price.”
However, he added that as research progresses, more compute will be required. “We are excited to continue executing on our research roadmap and believe that more compute is now more important than ever to succeed in our mission,” he added.
It makes sense, as the startup is part of the Stargate Project, which will build a $500 billion AI infrastructure in Texas.
“I believe that Jevons Paradox could actually make NVIDIA far bigger than what it currently is, as democratised AI expands the demand base of their GPUs. The current stock dip is a knee-jerk reaction,” said Tech Whisperer founder Jaspreet Bindra.
However, not everyone believes in Jevons Paradox. With the rise of small language models and reduced reliance on GPUs, users will soon be able to run them on mobile devices and laptops. “Jevon’s Paradox? Phones and low-end laptops will be running powerful 1.5B-parameter models within a year or two,” said KissanAI founder Pratik Desai.
Similarly, Mansi Gupta, senior analyst at Everest Group, said, “DeepSeek is likely to create ripple effects for chipmakers like NVIDIA, which could potentially witness reduced demand for their higher-end, premium chips, as the model creators optimise their models for better cost-performance ratio.”
However, even though the cost of intelligence will drop to zero, there will still be a demand for more compute, as AI applications continue to grow. Lower costs lead to stronger distribution, which in turn means broader application. And that, in turn, leads to more compute and more users.
NVIDIA revealed Project DIGITS, a new $3,000 small supercomputer, at CES 2025. It targets AI researchers, data scientists, and students. As more efficient models are developed, more customers will purchase the supercomputer and run LLMs locally.
Meanwhile, Meta CEO Mark Zuckerberg has announced plans to invest $60-65 billion in capital expenditure during 2025 to expand the company’s AI infrastructure and computing capabilities.
NVIDIA Loves China
At Donald Trump’s inauguration, a notable gathering of tech leaders was observed, including Tesla CEO Elon Musk, Amazon founder Jeff Bezos, Meta CEO Mark Zuckerberg, Alphabet CEO Sundar Pichai, and Apple CEO Tim Cook.
However, NVIDIA CEO Jensen Huang decided to skip the event and instead visited China, stopping in Beijing, Shenzhen, and Shanghai to celebrate the Lunar New Year with local staff.
Despite US chip export restrictions, Huang affirmed that NVIDIA remains dedicated to investing in China, where its workforce now totals approximately 4,000 employees, he mentioned at the Beijing office’s annual meeting.
China is an important market for NVIDIA. The company recently strongly criticised the Biden administration’s new “AI Diffusion” rule, set to impose restrictions on global access to AI chips and technology.
The US has banned the export of NVIDIA’s H800 to China and prevented the company from selling chips even with a reduced transfer rate. However, the GPUs are still smuggled to China.
While there is no official disclosure of the number of H800 GPUs exported to China, an investigation suggested that there is an underground network of around 70 sellers who claim to receive dozens of GPUs every month.
Meanwhile, NVIDIA has developed modified versions of its chips, such as the H20, L20, and L2, which comply with US export regulations. These chips are designed to have reduced capabilities compared to their full versions, allowing them to be legally sold in China.
Another report revealed that NVIDIA chips are being used in server products from Dell, Supermicro, and others in China. Recently, the US department of commerce asked NVIDIA to investigate how its products reached China.
The GPU giant also faces competition from local competitors like Huawei. With its Ascend series of data centre processors, particularly the Ascend 910B and the upcoming Ascend 910C, Huawei is actively working to challenge NVIDIA’s dominance in AI computing.
The company has informed potential clients that its upcoming Ascend 910C processor is on par with NVIDIA’s H100. Interestingly, while DeepSeek trained its models on NVIDIA H800 GPUs, it is now running inference on Huawei’s new domestic chip, the 910C.
