LMCache: An Efficient KV Cache Layer for Enterprise-Scale LLM Inference

An open-source KV cache layer for extracting, storing, offloading, transferring, and reusing caches across vLLM and SGLang engines. It reframes KV cache as a shared storage and communication layer rather than private state inside one inference engine.

LMCache is useful for visitors turning a local box into a shared service because cache reuse can beat buying more hardware.

It reframes KV cache as a shared storage and communication layer rather than private state inside one inference engine. The useful part for GPU Hunter readers is not the abstract result alone; it is the hardware implication: whether a model fits, whether a runtime can use the format, or whether throughput is limited by memory movement instead of arithmetic.

For hosted local-AI products, cache reuse and prefill-decode disaggregation can beat simply adding more GPUs. For long-context work, KV cache behavior is often the constraint that shows up after the model weights already fit. Cache precision, eviction, reuse, and memory movement can change the practical value of the same GPU.