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Bandwidth-Bound Decode Throughput
Because decode reads every weight once per step, its speed is a division: memory bandwidth over bytes per step. That one formula gives single-stream tokens per second for any model on any card, the batch curve that flattens at the ridge point, the effect of quantization, and the point where the KV cache rather than the weights becomes the thing being read. This page derives it, works it for a 70B model on four accelerators, and shows how to read a vendor throughput claim against it.
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Napkin Math, Cost & CapacityHow many tokens per second can a 70B model generate for a single user on H100s?→Napkin Math, Cost & CapacitySize an inference fleet for a 70B model serving 1,000 concurrent users→Napkin Math, Cost & CapacityHow much does moving from bf16 to fp8 save in serving cost?→Open-Weights Models & Serving EnginesYour model decodes at a tenth of its bandwidth bound at batch one. Explain the gap.→Napkin Math, Cost & CapacityHow big can the decode batch get before the step becomes compute-bound?→Napkin Math, Cost & CapacityHow many tokens per second can one B200 decode for a 70B model?→
