KV cache management in AI models
LMCache offers a layer that increases inference speed by optimizing key-value cache (KV cache) management for large language models. This system, which makes memory usage efficient, aims to reduce the computational load in queries that use the same context.
Updates
- November 6, 2026: Stars 12,074 → 12,292, latest release v0.5.7 (November 5, 2026).
- October 15, 2026: Stars 11,779 → 12,074, latest release v0.5.6 (October 14, 2026).
- September 13, 2026: Stars 11,587 → 11,779, latest release v0.5.5 (September 12, 2026).
- August 31, 2026: Stars 11,251 → 11,587, latest release operator-v0.5.4 (August 28, 2026).
What you get
- It shortens the first token time by increasing the inference speed in large language models.
- It reduces computational load by optimizing memory usage.
- It makes the KV cache persistent, allowing it to be reused across different sessions.
Installation
pip install lmcacheIf you don't write code
How can I optimize KV cache management for large language models using the LMCache library? What are the basic steps I should follow after installation to increase performance and make memory usage efficient, especially in long-context queries? Explain how I can integrate this layer into my existing inference system, considering the engine-independent working principle stated in the documentation.
Repository: https://github.com/LMCache/LMCache. Follow the official instructions in the README of this repository for setup and execution.
Before starting, check which OS we are on and if the necessary tools (e.g., Python, Node.js, Git, Docker) are installed. Install any missing ones step-by-step according to this system.
Create a new, empty folder to work in. Do not touch any files outside this folder and do not delete anything. Tell me what you are going to do in one sentence before running each command.
Tell me what I should see on the screen at the end of each step. If an error occurs, I will paste the error message to you exactly as it is, and we will fix it together.
Related dictionary terms
Links
TreScout did not build this tool · we found it in GitHub trends and wrote it up. This page describes the repository as of 2026-06-13: The star count and our text belong to that day, the repository may have changed since. Check the repository link for the current state. The introductory text was prepared with AI from the repository's own documentation, and this page was machine-translated from the Turkish original · the Turkish version prevails.