context caching

**Context caching** is the **serving optimization that reuses previously processed prompt context state to avoid recomputing identical prefixes** - it is a major latency and cost lever for repeated or multi-turn workloads. **What Is Context caching?** - **Definition**: Reuse of precomputed model state tied to prompt prefixes or session history. - **Cache Targets**: Typically stores KV tensors, prompt embeddings, or compiled prompt plans. - **Workload Fit**: Most beneficial for repeated system prompts, templates, and shared user prefixes. - **Serving Role**: Reduces prefill compute before token decoding begins. **Why Context caching Matters** - **Latency Gains**: Prefix reuse cuts time to first token for repeated contexts. - **Throughput Boost**: Saved prefill compute increases effective server capacity. - **Cost Reduction**: Less duplicate compute lowers hardware utilization per request. - **User Consistency**: Repeated flows become faster and more predictable. - **Scalability**: Context-heavy applications benefit significantly from cache reuse. **How It Is Used in Practice** - **Key Canonicalization**: Normalize prompts so semantically identical prefixes map to same cache key. - **Version Binding**: Invalidate caches when model, tokenizer, or system prompt versions change. - **Hit-Rate Monitoring**: Track cache efficiency and warmup behavior across traffic cohorts. Context caching is **a foundational optimization in modern LLM serving stacks** - robust context caching improves first-token latency and inference economics.