LMCache

On adoption, LMCache looks strong enough to merit real evaluation by infra-minded LLM teams. Its GitHub star count and broad X reposting are clear proof of attention, but that is heat proof, not usability proof. The stronger evidence comes from the official repo and product posts, which support that it is specifically a KV cache reuse/offload/prefetch layer for inference systems. It is not a vector database, not a prompt-management SaaS, and not a full replacement for a serving engine. A more accurate analogy is a reusable KV-cache control layer added on top of vLLM or SGLang.

In practice, it aims to avoid recomputing repeated prefixes and prior context by storing and reusing KV state across requests. The available evidence supports integrations with vLLM, SGLang, and TensorRT-LLM, plus features such as MP mode, P2P transfer, CPU/disk/S3 offload, and multi-platform support. Official and community posts consistently frame the benefit as better TTFT, throughput, and GPU efficiency. But claims like “14x faster” or “90% cheaper” mainly appear in social posts or demo-style summaries, so they should be treated as scenario-specific, not guaranteed production outcomes.