Researchers allege DeepSeek, Kimi, and other Chinese AI models were trained using banned Nvidia Blackwell GPUs

Researchers allege DeepSeek, Kimi, and other Chinese AI models were trained using banned Nvidia Blackwell GPUs

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The scientists are careful to hedge their findings, noting that while their math checks out, it does not reveal where the chips were, how they were accessed, or prove that export controls were broken. As far as we can tell, it's currently not illegal for Chinese companies to rent Blackwell hardware abroad, and even though Nvidia's grip is tightening , U.S. companies aren't strictly prohibited from doing so, as evidenced by the recent Tencent-Oracle deal .

This identification method is limited to models with open weights, and depends on both hardware and software stacks used for model training — and yet it's both clever and simple, like the Egg of Columbus .

Let's establish some base facts. First, different AI accelerator families have unique counts of shader modules (SMs), marginally analogous to CPU cores — Nvidia's Hopper chips have 132, Blackwell B series have 148, and Blackwell GBs come with 152. Second, while there's more than one software stack for training AI models, PyTorch and its CUDA-aware friends are by far the most widely used on the green team's hardware.

The first step for training a model is initializing its weights, or how strongly the model considers each individual input. In modern LLM architectures, their initialization goes through a randomization step in which their values are adjusted plus/minus pseudo-randomly, using cuRAND — as in, randomly, but a seed starting value results in the same adjustment figures every time.

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