Nvidia calls DeepSeek's work “an excellent AI advancement”, reiterating “inference requires significant numbers of Nvidia GPUs and high-performance networking”
Nvidia called DeepSeek's R1 model “an excellent AI advancement,” despite the Chinese startup's emergence causing …
CNBCKif Leswing
Context & Ripple Effects
DeepSeek had just emerged from High-Flyer’s research arm as a challenger to established U.S. AI companies, making Nvidia’s response more consequential than a routine supplier endorsement. DeepSeek’s rapid rise from a quant-fund research branch put the economics and infrastructure behind reasoning models into focus.
The subsequent coverage arc sharpened the same tension: DeepSeek pursued models customized for Chinese-made chips while continuing to sit within Nvidia-centered compute supply chains. Its V3.1 customization for next-generation Chinese chips shows why Nvidia’s emphasis on inference hardware matters beyond the initial R1 release.
First-order effects
Nvidia publicly validates R1 as an AI advance while arguing that deploying such models still requires large GPU fleets and high-performance networking.
The statement frames DeepSeek’s efficiency narrative as compatible with, rather than directly destructive to, Nvidia’s inference demand thesis.
Second-order effects
Inference customers and competing accelerator vendors face a clearer test: demonstrate that model efficiency can lower deployed-compute needs, not merely training expense.
DeepSeek’s ability to tailor models to alternative hardware raises the value of software–chip co-optimization for Chinese AI infrastructure providers.
Third-order effects
If efficient reasoning models proliferate, AI competition may shift from headline training runs toward sustained inference economics, networking, and hardware-software integration.
The longer-term outcome remains uncertain: lower cost per model can reduce compute intensity per task while expanding the number of tasks economical to serve.
The trend: Efficient AI models are turning inference capacity and integrated deployment stacks into the central battleground for AI infrastructure vendors.
Has anyone heard of someone significant canceling a big NVDA purchase order because of DeepSeek? That would be meaningful true canary but I have a hard time imagining it in the short term given: • big hardware spends are budgeted far in advance • no team would choose efficienc…
Jevons paradox strikes 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. https://en.m.wikipedia.org/...
I feel this should be a much bigger story: DeepSeek has trained on Nvidia H800 but is running inference on the new home Chinese chips made by Huawei, the 910C. [image]
The only REAL explanation as to why Nvidia, $NVDA, crashed today: 1. Clearly, DeepSeek has become a leader in AI innovation with Nvidia calling it an “excellent AI advancement” 2. In theory, AI advancement is BULLISH for Nvidia because it means more chip demand 3. The issue is
$NVDA statement on DeepSeek “DeepSeek is an excellent AI advancement and a perfect example of Test Time Scaling. 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 most ironic part about DeepSeek vs OpenAI: 1. OpenAI was developed by a non-profit and costs $200/month 2. DeepSeek was developed by a hedge fund and costs $0/month 3. OpenAI is actually closed-AI 4. DeepSeek is actually open-AI Talk about a turn of events.
Unable to dismiss DeepSeek on technical grounds, the main criticism being levelled against it is that it declines to give a proper account of the 1989 Tiananmen Square massacre. This is presented as a sharp contrast with similar programs developed in the “free world”. It is
I fully expect our inferiority complex and a belief that Nvidia was all smoke and mirrors, will cause people to hit the bid, Nvidia and all the others. They have to, they fear “sputnik.” Yes, let's politicize the whole thing
It's hard to believe, but due to H100 restrictions, DeepSeek was forced to train R1 manually, with thousands of Chinese citizens holding flags to act as logic gates. [image]
The probability U.S. AI leaders will reduce AI infrastructure build for training because of a distilled copycat DeepSeek model is negligible. Inference compute supply is a legitimate question in the short term but developers will fill the void. My DeepSeek take is live [image]
The key is really this: AI usefulness scales logarithmically with inference time compute. Right now for many use cases the amount of compute you need to operate at human-level is such that AI isn't economically viable for that use case. The more compute efficient AI gets, the m…