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A KV Cache Management Layer for Scalable LLM Inference
  
    

  
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 Updates
- [2026/05] 🔥 Agentic workload benchmark on AMD MI300X (blog).
- [2026/04] 🔥 LMCache's new multiprocess (MP) architecture release (blog).
- [2026/03] LMCache at GTC 2026 (post).
- [2026/01] LMCache multi-node P2P CPU memory sharing, from experimental feature to production (blog).


More

- [2025/11] LMCache x CoreWeave accelerate efficient LLM inference for Cohere (blog).
- [2025/10] LMCache joins the PyTorch Foundation and Tensormesh unveiled (blog, PyTorch).
- [2025/09] NVIDIA Dynamo integrates LMCache, accelerating LLM inference (blog).
- [2025/08] 🎉 LMCache hits 5,000+ GitHub stars (blog).
- [2025/08] LMCache supports gpt-oss (20B/120B) on day 1 (blog).
- [2025/07] Get faster LLM inference and cheaper responses with LMCache and Redis (Redis blog).
- [2025/07] LMCache extends its turbo-boost to multimodal models in vLLM V1 (blog).
- [2025/06] LLM Production Stack goes cross-hardware: AMD, Arm and Ascend (blog).



 About

LMCache is a KV cache management layer for LLM inference. It turns KV cache from a temporary state into reusable AI-native knowledge that can be stored persistently, reused across multiple serving engines, monitored with an observability stack, and transformed for better generation quality. As a result, LMCache reduces TTFT (time-to-first-token) and improves throughput, especially for long-context agentic, multi-turn conversation, and knowledge-augmented workloads (e.g., RAG).

LMCache is vendor-neutral. It can be used as a KV cache layer for a range of mainstream open-source serving engines, inference frameworks, hardware vendors, storage systems, and infrastructure providers. The vendor neutrality allows users to freely switch between serving engines and storage vendors, while reusing the stored KV caches.



  
  
  



 Key features

- Engine-independent deployment: LMCache, as a standalone daemon process, manages KV cache independently from the inference engine process, so that KV cache will not be lost even if the inference engine crashes (i.e., no fate-sharing with engines).

- Persistent, tiered KV cache offloading and reuse: Move KV caches out of GPU memory into a tiered storage hierarchy spanning CPU memory, local storage, and remote backends, enabling reuse across requests, sessions, and engine instances to reduce repeated prefill computation and improve TTFT.

- Production-level KV cache observability: LMCache provides a rich set of KV cache observability metrics, including typical Kubernetes metrics (health monitoring, performance diagnostics), KV-cache-specific metrics (request-level and token-level prefix cache hits, lifecycle, request-level KV cache performance), management metrics (user-specific us