kv cache optimization

**KV cache optimization** is the **set of techniques that improve memory efficiency, access speed, and reuse behavior of key-value attention caches during autoregressive decoding** - it is central to high-throughput LLM inference. **What Is KV cache optimization?** - **Definition**: Engineering of KV storage layout, precision, paging, and eviction for fast decode loops. - **Optimization Targets**: Memory footprint, bandwidth use, lookup latency, and cache reuse rate. - **Decode Dependency**: Autoregressive generation reuses KV state every token step. - **System Scope**: Spans model kernels, runtime allocators, and scheduler behavior. **Why KV cache optimization Matters** - **Performance**: KV operations dominate decode-time latency for long sequences. - **Capacity**: Better cache efficiency allows more concurrent requests per GPU. - **Cost Control**: Memory optimizations increase tokens-per-dollar in production serving. - **Stability**: Poor cache management leads to fragmentation and unpredictable tail latency. - **Feature Enablement**: Advanced serving methods rely on efficient KV handling. **How It Is Used in Practice** - **Paged Allocation**: Use fixed-size blocks to reduce fragmentation and speed memory reuse. - **Precision Strategy**: Apply mixed precision where quality impact is validated. - **Access Profiling**: Measure bandwidth and hit behavior to tune kernel and scheduler settings. KV cache optimization is **the performance core of production autoregressive inference** - well-tuned KV pipelines unlock major latency, throughput, and cost gains.