vLLM guide details production KV cache tuning

Getting an LLM checkpoint to load into VRAM is only the initial hurdle in AI deployment; the core engineering challenge is budgeting dynamic KV cache capacity to prevent catastrophic preemption under concurrent production workloads. Unlike static model weights, KV cache demand balloons dynamically with context length and active users, causing unmanaged deployments to crash with out-of-memory errors. When KV memory runs dry, vLLM must swap tokens to CPU memory or recompute attention vectors, destroying P95 latency under heavy user queues. Running the KV cache in FP8 roughly halves the memory footprint compared to 16-bit precision, unlocking critical headroom for concurrency on modern GPU architectures. Furthermore, capping context limits to realistic traffic needs rather than theoretical brochure maximums preserves concurrent request slots, while prefix caching multiplies effective capacity for agentic and RAG workflows without extra hardware.