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LMCache

TOOL

Supercharge LLM inference with the fastest KV Cache layer. 3-10x delay savings and GPU cycle reduction for multi-round QA and RAG. Integrates seamlessly with vLLM for distributed, high-throughput deployments. Apache 2.0 licensed

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Overview

LMCache: a free, copy-ready tool on OpenRuna. Supercharge LLM inference with the fastest KV Cache layer. 3-10x delay savings and GPU cycle reduction for multi-round QA and RAG. Integrates seamlessly with vLLM for dis

What this tool does

"LMCache" packages a proven tool so you can skip the trial-and-error of writing one from scratch. Supercharge LLM inference with the fastest KV Cache layer. 3-10x delay savings and GPU cycle reduction for multi-round QA and RAG. Integrates seamlessly with vLLM for distributed, high-throughput deployments. Apache 2.0 licensed OpenRuna cross-links it to related prompts, agents, and tools, which makes assembling a full workflow around it straightforward. Paste it straight into a chat, drop it into a system prompt, or store it as a reusable skill.

Use cases

  • Keep it in a shared library as the canonical version of this tool for your organisation.
  • Fork it as a baseline and layer in your own project context, constraints, and examples.
  • Reach for it during planning or review sessions when you want consistent, AI-assisted structure.
  • Combine it with related tools and prompts in the same OpenRuna category to build an end-to-end workflow.

Example output

Ask the model to apply "LMCache" to your scenario and it returns a structured answer — clear sections, actionable steps, and assumptions stated upfront — ready to paste into docs, tickets, or code comments. A short follow-up turn usually tightens the result to exactly what you need.

Tips by platform

Claude

In Claude, paste the full tool as your first message or add it to Project instructions, then ask Claude to confirm assumptions before it executes. For longer tools, iterate inside the artifact panel.

ChatGPT

For ChatGPT, save this tool as a Custom Instruction or a saved prompt so it is one click away. Add your specifics in a follow-up rather than editing the original.

Cursor

Cursor users can store this tool as a project rule so Agent mode applies it automatically. Mention it with @ when you want it scoped to a single task.

Frequently asked questions

What is "LMCache"?
It is a tool listed on OpenRuna — Supercharge LLM inference with the fastest KV Cache layer. 3-10x delay savings and GPU cycle reduction for multi-round QA and RAG. Integrates seamlessly with vLLM for distributed, high-throughput deployments. Apache 2.0 licensed You can copy and adapt it for ChatGPT, Claude, Cursor, or any other AI assistant.
Is "LMCache" free to use?
Most OpenRuna resources are open or CC0-licensed. Check the license shown on this page before commercial use; premium collections are clearly marked as such.
How do I get the best results from this tool?
Replace any placeholders, add your project context, and ask the model to confirm its assumptions first. Iterate over 2–3 follow-up turns rather than expecting a perfect first response.
Does "LMCache" work with both Claude and ChatGPT?
Yes — it is model-agnostic text, so it runs on Claude, ChatGPT, Gemini, and Cursor. The tips on this page cover each of those assistants specifically.
Where can I find resources related to "LMCache"?
Scroll to the Related resources section on this page, or open the matching category hub on OpenRuna to find connected prompts, tools, agents, and datasets in the same topic area.

Related resources