Former OpenAI researcher says he found Nvidia Blackwell fingerprints hidden inside six Chinese AI models

AI researcher Will DePue says a forensic analysis has turned up the first technical evidence that leading Chinese AI labs have access to Nvidia’s latest Blackwell hardware despite US export controls. He says senior researchers at OpenAI and Anthropic have already checked the method.

According to a forensic investigation by Will DePue, Chinese AI companies DeepSeek, Moonshot AI, MiniMax, and Ant Group are using Nvidia’s export-controlled Blackwell GPUs. DePue played a key role at OpenAI in building the Sora and Sora 2 video models as well as GPT-4o, o3, and GPT-5 before founding the nonprofit research group Unemployment Labs. His analysis, he says, provides the first technical evidence of this access. US officials and news outlets had suspected that DeepSeek and Moonshot AI were using Blackwell chips but never published proof. DePue wrote on X that senior researchers at OpenAI and Anthropic have already independently checked and confirmed his findings. According to DePue, six Chinese models released since April 2026 carry the distinct signature of Nvidia’s Blackwell generation, while earlier models such as DeepSeek-V3 and Kimi K2.5 show traces of Nvidia’s older Hopper generation. The evidence comes directly from the published language models. An AI model consists of billions of numerical values called weights. Before training starts, PyTorch, the standard software framework, fills these empty slots with random numbers generated from a starting value called a seed. To distribute this massive amount of data quickly, the GPU splits the work across its individual compute cores. Because different chip models have different core counts, the filling process produces a characteristic pattern, according to the report. Older Hopper chips such as the H100 and H800 use 132 compute cores, while the newer Blackwell chips use exactly 148 or 152 cores depending on the model (B200/B300 or GB200/GB300). Training then adjusts these values millions of times, but some slots for unused words or symbols keep their initial random values. Even in heavily trained parts of the model, a faint statistical echo of the initial distribution survives, according to DePue. He says he worked the pattern backward and reproduced the results for DeepSeek-V4-Flash and Kimi K3 exactly on real Blackwell chips.