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This articles that distills a report which doesn't bring anything new to the table. I can't load their site right now, but in their release I recall they explicitly excluded experimentation and R&D costs. And have $1.6b worth of hardware is not the same as spending $1.6b on costs to build the model.

The article says:

> despite the company's claims that DeepSeek only cost $6 million and 2,048 GPUs to train. However, industry analyst firm SemiAnalysis reports that the company behind DeepSeek incurred $1.6 billion in hardware costs and has a fleet of 50,000 Nvidia Hopper GPUs, a finding that undermines the idea that DeepSeek reinvented AI training and inference with dramatically lower investments than the leaders of the AI industry.

The underlying report states:

> The $6M cost in the paper is attributed to just the GPU cost of the pre-training run, which is only a portion of the total cost of the model. Excluded are important pieces of the puzzle like R&D and TCO of the hardware itself. > > https://semianalysis.com/2025/01/31/deepseek-debates/

But we already know this. The DeepSeek paper even gives a breakdown:

> Pre-Training: 2664K > Context Extension: $0.238M > Post-Training: $0.01M > Total: $5.576M > > https://arxiv.org/pdf/2412.19437v1






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