The Deepseek Cover Up
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As Fortune experiences, two of the teams are investigating how DeepSeek manages its stage of functionality at such low costs, whereas another seeks to uncover the datasets DeepSeek makes use of. Consequently, our pre-training stage is accomplished in less than two months and prices 2664K GPU hours. First, we have to contextualize the GPU hours themselves. A second level to think about is why DeepSeek is training on solely 2048 GPUs while Meta highlights training their model on a larger than 16K GPU cluster. Many of these particulars had been shocking and intensely unexpected - highlighting numbers that made Meta look wasteful with GPUs, which prompted many online AI circles to kind of freakout. This post revisits the technical details of DeepSeek V3, but focuses on how greatest to view the price of training fashions on the frontier of AI and the way these prices could also be altering. We’ll get into the specific numbers beneath, but the question is, which of the many technical improvements listed in the DeepSeek V3 report contributed most to its learning effectivity - i.e. mannequin efficiency relative to compute used.
It specializes in allocating totally different tasks to specialised sub-models (consultants), enhancing efficiency and effectiveness in handling numerous and complicated problems. That is the uncooked measure of infrastructure effectivity. Note that tokens outside the sliding window still influence next word prediction. If a duplicate word is tried to be inserted, the perform returns without inserting something.