KV Cache Optimization

During decoding, a transformer can reuse previously computed attention keys and values instead of recomputing them for every new token. The cache grows with sequence length, layers, attention heads and representation size. Paged allocation stores blocks flexibly rather than requiring a large contiguous reservation, while prefix sharing can reuse valid identical context. Quantization or eviction changes storage differently and may introduce approximation. KV caching is distinct from caching final answers: generation still occurs. Optimization therefore includes allocation, lifecycle and precision decisions, and must preserve correct association between each request and its cached context.

The practitioner measures cache memory under representative context lengths and concurrency, then selects allocation and precision policies supported by the engine. Prefix reuse must match tokens and relevant model configuration exactly. Useful outputs include a memory budget, cache configuration and tests for varied sequence lengths. Quantized or compressed caches need task-quality comparison, especially long-context cases. Monitoring should reveal eviction, preemption and allocation failures so a memory-saving choice does not quietly produce repeated recomputation or unstable latency.

Paged allocation reduces fragmentation but does not remove the cache's underlying growth. Prefix reuse benefits depend on actual repeated token sequences. Quantization and eviction can change accuracy, unlike exact allocation changes. Quality requires correct cache identity, realistic memory accounting and behavior checks for approximations. Confusing weight memory with KV memory can misdiagnose capacity problems, and a cache setting suitable for short requests may fail when long conversations occupy the same serving instance.