America tried to sell China H20s and China decided they didn’t want them and now Nvidia is halting related orders with suppliers.
I've not been following this. Is it possible this is a national security concern, as in China has the same fear America does that the other country is hiding spywear in the chips.
I suspect DeepSeek is unusually vulnerable to the problem of switching hardware because my expectation for their cost advantage fundamentally boils down to having invested a lot of effort in low-level performance optimization to reduce training/inference costs.
Switching the underlying hardware breaks all this work. Further, I don't expect the Huawei chips to be as easy to optimize as the Nvidia H-series, because the H-series are built mostly the same way as Nvidia has always built them (CUDA), and Huawei's Ascend is supposed to be a new architecture entirely. Lots of people know CUDA; only Huawei's people know how the memory subsystem for Ascend works.
If I am right, it looks like they got hurt by bad timing this round same way as they benefited from good timing last round.
Edit: Finally found a reasonable description of what happened. They were programming Nvidia hardware in assembly. My hardware switch guess is confirmed - this has wiped out their primary advantage. If they continue to fade, I think we could fairly assess them as a casualty of politics.
What if DeepSeek released a model claiming 66 on SWE and almost no one tried using it? Would it be any good? Would you be able to tell? Or would we get the shortest post of the year?
Eleanor Olcott and Zijing Wu: Chinese artificial intelligence company DeepSeek delayed the release of its new model after failing to train it using Huawei’s chips, highlighting the limits of Beijing’s push to replace US technology.
DeepSeek was encouraged by authorities to adopt Huawei’s Ascend processor rather than use Nvidia’s systems after releasing its R1 model in January, according to three people familiar with the matter.
But the Chinese start-up encountered persistent technical issues during its R2 training process using Ascend chips, prompting it to use Nvidia chips for training and Huawei’s for inference, said the people.
The issues were the main reason the model’s launch was delayed from May, said a person with knowledge of the situation, causing it to lose ground to rivals.
The real world so often involves people acting so much stupider than you could write into fiction.
America tried to sell China H20s and China decided they didn’t want them and now Nvidia is halting related orders with suppliers.
DeepSeek says that the main restriction on their development is lack of compute, and the PRC responds not by helping them get better chips but by advising them to not use the chips that they have, greatly slowing things down at least for a while.
DeepSeek: Introducing DeepSeek-V3.1: our first step toward the agent era! Hybrid inference: Think & Non-Think — one model, two modes
Faster thinking: DeepSeek-V3.1-Think reaches answers in less time vs. DeepSeek-R1-0528
Stronger agent skills: Post-training boosts tool use and multi-step agent tasks
Try it now — toggle Think/Non-Think via the “DeepThink” button.
API Update deepseek-chat → non-thinking mode
deepseek-reasoner → thinking mode
128K context for both
Anthropic API format supported.
Strict Function Calling supported in Beta API.
More API resources, smoother API experience
Tools & Agents Upgrades Better results on SWE / Terminal-Bench
Stronger multi-step reasoning for complex search tasks
Big gains in thinking efficiency
V3.1 Base: 840B tokens continued pretraining for long context extension on top of V3
Tokenizer & chat template updated — new tokenizer config.
V3.1 Base Open-source weights.
V3.1 Open-source weights.
Pricing Changes New pricing starts & off-peak discounts end at Sep 5th, 2025, 16:00 (UTC Time)
Until then, APIs follow current pricing
Pricing page.
Teortaxes: for now seems to have the same performance ceiling as 0528, maybe a bit weaker on some a bit stronger on other problems. The main change is that it’s a unified merge that uses ≥2x fewer reasoning tokens. I take it as a trial balloon before V4 that’ll be unified out of the box.
Wes Roth: DeepSeek has quietly published V 3.1, a 685-billion-parameter open-source model that folds chat, reasoning, and coding into a single architecture, handles 128 k-token context windows, and posts a 71.6 % score on the Aider coding benchmark edging out Claude Opus 4 while costing ~68× less in inference.
But these two data points don’t seem backed up by the other reactions, or especially the lack of other reactions, or some other test results.
Artificial Analysis has it coming in at 60 versus r1’s 59, which would be only a small improvement.
Hasan Can said it hallucinates a lot. Steve Strickland says ‘it’s the worst LLM I’ve even tried’ complaining about it failing a mundane task, which presumably was very bad luck.
I tried to conduct Twitter polls, but well over 90% of respondents had to click ‘see results’ which left me with only a handful of real responses and means Lizardman Constant problems and small sample size invalidate the results, beyond confirming no one is looking, and the different polls don’t entirely agree with each other as a result.
If this were most open model companies, I would treat this lack of reaction as indicating there was nothing here, that they likely targeted SWE as a benchmark, and move on.
Since it is DeepSeek, I give them more credit than that, but am still going to assume this is only a small incremental upgrade that does not change the overall picture. However, if 3.1 really was at 66-level for real in practice, it has been several days now, and people would likely be shouting it from the rooftops. They’re not.
How Should We Update?
Even if no one finds anything to do with it, I don’t downgrade DeepSeek much for 3.1 not impressing compared to if they hadn’t released anything. It’s fine to do incremental improvements. They should do a v3.1 here.
The dumbest style of reaction is when a company offers an incremental improvement (see: GPT-5) and people think that means it’s all over for them, or for AI in general, because it didn’t sufficiently blow them away. Chill out.
It’s also not fair to fully pin this on DeepSeek when they were forced to do a lot of their training this year on Huawei Ascend chips rather than Nvidia chips. Assuming, that is, they are going to be allowed to switch back.
Either way, the clock is ticking on v4 and r2.
Hybrid inference: Think & Non-Think — one model, two modes
Faster thinking: DeepSeek-V3.1-Think reaches answers in less time vs. DeepSeek-R1-0528
Stronger agent skills: Post-training boosts tool use and multi-step agent tasks Try it now — toggle Think/Non-Think via the “DeepThink” button. API Update
deepseek-chat → non-thinking mode
128K context for both
Anthropic API format supported.
Strict Function Calling supported in Beta API.
Better results on SWE / Terminal-Bench
Stronger multi-step reasoning for complex search tasks
V3.1 Base Open-source weights.
Pricing page. Teortaxes: for now seems to have the same performance ceiling as 0528, maybe a bit weaker on some a bit stronger on other problems. The main change is that it’s a unified merge that uses ≥2x fewer reasoning tokens. I take it as a trial balloon before V4 that’ll be unified out of the box.
