KV Caching in LLMs

Short summary

KV caching stores pre-computed key and value vectors from previous tokens, eliminating redundant computation during generation. This trades GPU memory for compute efficiency, making inference practical at scale. Understanding it is essential for managing model latency and concurrency constraints.

• •KV caching eliminates recomputation of key/value vectors by caching them after prefill phase
• •Trades GPU memory for compute efficiency, dramatically reducing inference time after first token
• •Critical for production LLM systems managing concurrency, context length, and latency tradeoffs

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