ModelTC

LightLLM: Serve Large Language Models

LightLLM is a Python framework for running and serving large language models. It combines model inference with serving features for teams building scalable LLM endpoints or experimenting with serving systems.

ActivePythonApache-2.0LLMMachine LearningFramework
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Stars
4.3k
Forks
372
Stars, last 21 days
+35
Last commit
today
Contributors
52

Our take

Activeregular commits and releases

Health score

68/100

How it is scored
Activity
65
Community
76
Issues
53
Pull requests
76

+35 stars in the last 21 days

  • Merges more pull requests than 79% of the projects we track

LightLLM is an actively developed, research-informed serving framework with a substantial contributor base, suited to teams comfortable evaluating an evolving inference stack.

Good fit if

  • You need Python-based LLM inference and serving, and want a framework designed for scalable deployment.
  • Your work benefits from token-level KV cache management, request scheduling, or constrained decoding.
  • You can assess model and hardware compatibility using the project documentation, and prefer an Apache-2.0 licensed project.

Look elsewhere if

  • You need a serving solution whose requirements and deployment setup are fully specified in this overview, rather than checking the project documentation.
  • Your project does not involve LLM inference or serving.
  • You require a project with no unresolved maintenance questions; LightLLM has an open issue backlog, although recent development and releases indicate active maintenance.
All health signals
Last commit2026-10-13 (today)
Commits, last 90 days100+
Releases, last 12 months1 (latest v1.2.0, 2026-08-10)
Contributors52 (top contributor: 33% of commits)
Issues closed, last 90 days2+
Pull requests merged, last 90 days64 (typically merged in 0 days)
Project age3 years

Checked on 2026-10-14 with the GitHub API.

Overview

LightLLM is a Python-based framework for LLM inference and serving. It aims to make it easier to deploy models behind services while supporting performance-oriented serving and scaling, rather than requiring teams to assemble those pieces from scratch.

It is aimed at developers and researchers who need to serve models or work on serving infrastructure. Its pure-Python design and token-level KV cache management also make it relevant as a base for research projects.

Key Features

  • Provides a framework for LLM inference and serving.
  • Supports scaling serving deployments.
  • Includes token-level KV cache management.
  • Offers constrained decoding for structured LLM generation.
  • Includes request scheduling designed around service-level guarantees.
  • Provides documentation in English and Chinese, plus a quick start and deployment tutorial.

Use Cases

  • Serving an LLM behind an application endpoint, for teams deploying model-backed products.
  • Operating model-serving infrastructure where request scheduling and service-level guarantees matter.
  • Exploring structured generation with constrained decoding, for developers who need deterministic output formats.
  • Prototyping serving research that involves KV cache management, for researchers building on a Python-based system.

What you need

Detected in the repository

  • Python (from setup.py)
  • A test suite and automated checks on GitHub Actions

License in plain words

Apache-2.0permissive

  • Commercial use: yes
  • Modify and redistribute: yes
  • You must keep: the license, the NOTICE file and a note of your changes
  • Share your changes: no
  • Includes an explicit patent grant from the contributors:

A summary, not legal advice: the LICENSE file is what applies.

Getting Started

Start with the Quick Start guide. See the installation instructions for setup details, and the README for project links and further information.

Considerations

The project is actively maintained: recent commit activity is high, it has had a release within the past year, and work includes contributions from dozens of people rather than depending primarily on one contributor. However, the measured issue-close data does not establish a typical resolution time, so teams should review current issues and test their own deployment requirements.

The repository information does not specify a universal hardware or model requirement. Check the installation guide and deployment documentation for the environment needed by your chosen model and configuration. LightLLM is licensed under Apache-2.0; review the license and any obligations relevant to your deployment.

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