DeepSeek’s inference costs 12x more than training, YC Paper Club estimates
A Y Combinator Paper Club session compared training and inference costs using DeepSeek’s public data. The group found that while training DeepSeek V3 took about 2.8 million H800 hours, daily inference already consumes 43,000 H800 hours—and could be higher. Over two years, inference’s annual compute volume becomes 12 times that of training, the session noted. The analysis highlights that…
Key points
- DeepSeek’s training used 2.8M H800 hours; inference now runs 6x that daily, 12x annually
- Inference efficiency boosts margins: DeepSeek claims **80% profit** from inference optimizations like MoE and RadixAttention
- Open models (e.g., Together AI’s **400T tokens/month**) cut costs by **10x** but vary in quality due to quantization or engine differences
Model pages: Kimi K3 → · GPT-6 →
The story so far
4 episodes →- DeepSeek’s inference costs 12x more than training, YC Paper Club estimatesthis story
"Inference is 12 times more important than training" — YC Paper Club estimates based on DeepSeek's public figures: training takes about 2.8 million H800 hours, while
note.com · 26 October 2026
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