How to Calculate KV Cache Memory

What the KV cache stores

Autoregressive generation reuses attention keys and values from earlier tokens instead of recomputing them for every new token. The retained tensors form the KV cache. It grows while a sequence grows and is released when that sequence finishes.

A useful estimate depends on layer count, hidden or head dimensions, KV head count, sequence length, concurrent sequences, and cache precision. Grouped-query attention can reduce the KV head component substantially.

A practical estimate

A common approximation multiplies two tensors—key and value—by layers, tokens, effective KV width, bytes per value, and concurrent sequences. Architecture details differ, so use the model configuration rather than guessing from parameter count.

Context capacity is not the same as typical context usage. Model both a normal traffic distribution and a worst-case admission limit.

Ways to reduce it

Shorter maximum sequences, prefix reuse, cache quantization, paged attention, and better admission control can reduce pressure. Each technique has quality, compatibility, or operational tradeoffs that should be tested in the target runtime.

Measure the deployed system

Calculator results are planning estimates. Measure peak allocated and reserved memory under real concurrency, prompt lengths, output lengths, and cancellation behavior before setting production capacity.