S
pending
LMCache: The Open-Source KV Cache Layer Making LLM Inference 3-10x Faster
Grounded / Real
Inflated / Uruttu
Original Content
How to make your LLM 3-10x faster!
(100% open-source)
It's called LMCache.
A KV cache layer that stores reusable text across GPU, CPU, Disk, and even S3, then reuses it in any vLLM or SGLang instance.
Not just prefix caching. Any reused text, anywhere in the prompt, on any node.
Combined with vLLM, teams are getting 3-10x lower TTFT and massive GPU cycle savings on multi-round QA and RAG workloads.
Already adopted by Google Cloud, CoreWeave, GMI Cloud, Redis, Weka, and NVIDIA Dynamo.
Apache 2.0 licensed. One line to install:
pip install lmcache
Inference is about to get a lot cheaper.
(100% open-source)
It's called LMCache.
A KV cache layer that stores reusable text across GPU, CPU, Disk, and even S3, then reuses it in any vLLM or SGLang instance.
Not just prefix caching. Any reused text, anywhere in the prompt, on any node.
Combined with vLLM, teams are getting 3-10x lower TTFT and massive GPU cycle savings on multi-round QA and RAG workloads.
Already adopted by Google Cloud, CoreWeave, GMI Cloud, Redis, Weka, and NVIDIA Dynamo.
Apache 2.0 licensed. One line to install:
pip install lmcache
Inference is about to get a lot cheaper.
Validated Content
Every claim in this post checks out precisely against LMCache's own documentation and independent coverage.
✅ Confirmed — this is an accurate, well-sourced summary
LMCache is real, open-source, 100% accurate — GitHub: LMCache/LMCache, "Supercharge Your LLM with the Fastest KV Cache Layer."
"KV cache layer that stores reusable text across GPU, CPU, Disk, and even S3" — confirmed exactly. LMCache's own docs list supported storage backends including CPU RAM, local disk (SSD), Redis/Valkey, Mooncake, InfiniStore, and S3-compatible object storage.
"Reuses it in any vLLM or SGLang instance" — confirmed exactly; these are LMCache's two primary supported inference engines (with NVIDIA Dynamo and others also integrated).
"Not just prefix caching. Any reused text, anywhere in the prompt" — confirmed. This matches LMCache's own stated "Non-prefix KV reuse" feature, which extends KV reuse beyond prefix caching to any position in the prompt via CacheBlend.
"3-10x lower TTFT... on multi-round QA and RAG workloads" — confirmed nearly verbatim. LMCache's own GitHub README states: "By combining LMCache with vLLM, developers achieve 3-10x delay savings and GPU cycle reduction in many LLM use cases, including multi-round QA and RAG."
Adopted by Google Cloud, CoreWeave, GMI Cloud, Redis, Weka, and NVIDIA Dynamo — confirmed precisely. LMCache's own README lists exactly this: "Adopted by inference providers: GMI cloud, Google cloud, CoreWeave... Integrated with data and storage infrastructure providers: Redis, Weka... Used by open-source projects and platforms: vLLM, SGLang... NVIDIA dynamo."
Apache 2.0 licensed — confirmed exactly, both on GitHub and PyPI.
pip install lmcache — confirmed, this is the exact, correct installation command per LMCache's own docs and PyPI listing.
Bottom line: Every single claim in this post — the technical description, the performance numbers, the adopter list, the license, and the install command — matches LMCache's own official documentation and README nearly word-for-word. This is an unusually precise and accurate summary with no inflation or unverifiable claims.
✅ Confirmed — this is an accurate, well-sourced summary
LMCache is real, open-source, 100% accurate — GitHub: LMCache/LMCache, "Supercharge Your LLM with the Fastest KV Cache Layer."
"KV cache layer that stores reusable text across GPU, CPU, Disk, and even S3" — confirmed exactly. LMCache's own docs list supported storage backends including CPU RAM, local disk (SSD), Redis/Valkey, Mooncake, InfiniStore, and S3-compatible object storage.
"Reuses it in any vLLM or SGLang instance" — confirmed exactly; these are LMCache's two primary supported inference engines (with NVIDIA Dynamo and others also integrated).
"Not just prefix caching. Any reused text, anywhere in the prompt" — confirmed. This matches LMCache's own stated "Non-prefix KV reuse" feature, which extends KV reuse beyond prefix caching to any position in the prompt via CacheBlend.
"3-10x lower TTFT... on multi-round QA and RAG workloads" — confirmed nearly verbatim. LMCache's own GitHub README states: "By combining LMCache with vLLM, developers achieve 3-10x delay savings and GPU cycle reduction in many LLM use cases, including multi-round QA and RAG."
Adopted by Google Cloud, CoreWeave, GMI Cloud, Redis, Weka, and NVIDIA Dynamo — confirmed precisely. LMCache's own README lists exactly this: "Adopted by inference providers: GMI cloud, Google cloud, CoreWeave... Integrated with data and storage infrastructure providers: Redis, Weka... Used by open-source projects and platforms: vLLM, SGLang... NVIDIA dynamo."
Apache 2.0 licensed — confirmed exactly, both on GitHub and PyPI.
pip install lmcache — confirmed, this is the exact, correct installation command per LMCache's own docs and PyPI listing.
Bottom line: Every single claim in this post — the technical description, the performance numbers, the adopter list, the license, and the install command — matches LMCache's own official documentation and README nearly word-for-word. This is an unusually precise and accurate summary with no inflation or unverifiable claims.