How KV Caching Accelerates LLMs
bdsqlsz · x · 2026-07-12
This article explains why KV caching drastically accelerates generation in LLMs.
A common observation is that when sending a request via ChatGPT or Claude, the first token is usually the slowest, while subsequent tokens stream rapidly. The article clarifies that this is intentional: during inference, the model reuses previously computed Key/Value states to avoid redoing extensive prefix calculations for every new token.
The piece breaks down this mechanism to help readers understand:
- Why Time-To-First-Token (TTFT) latency is higher
- The role of KV cache in inference acceleration
- How this design enables highly efficient subsequent token generation
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