LMCache spotlights shared KV cache economics

This is less about a single feature than a shift in how LLM infra gets priced: inference is becoming a storage-and-reuse problem, not just a compute problem.

• –Shared KV caches cut repeated prefill cost for long prompts, RAG pipelines, and multi-turn sessions
• –The upside depends on cache hit rate, memory tiering, and how much orchestration overhead the serving stack can absorb
• –For local and self-hosted LLM teams, this pushes the stack toward cluster-level reuse instead of isolated per-node caches
• –LMCache matters because it operationalizes that model with integrations across serving engines like vLLM