Like a caching layer for large-scale production inference of LLMs.
LMCache implements smart KV cache management by reusing key-value states of already encountered text between GPUs, CPUs, and local disks.
🟢 What it can do?
It can reuse any repetitive text fragments, not just prefixes.
This results in:
☞ 4–10x cost reduction in RAG for models owned by the user
☞ Lower Time-To-First-Token (TTFT)
☞ Higher throughput under load
☞ More efficient work with long-context scenarios
An illustrative example of application: NVIDIA integrated LMCache into its inference project Dynamo.
LMCache allows Dynamo to offload the KV cache to external storage layers and effectively reuse it between requests. This reduces the cost of prefill and frees up GPU memory for active computations.
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