lmdeploy

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LMDeploy is a toolkit for compressing, deploying, and serving LLM, developed by the MMRazor and MMDeploy teams. It has the following core features:

• Efficient Inference: LMDeploy delivers up to 1.8x higher request throughput than vLLM, by introducing key features like persistent batch(a.k.a. continuous batching), blocked KV cache, dynamic split&fuse, tensor parallelism, high-performance CUDA kernels and so on.

• Effective Quantization: LMDeploy supports weight-only and k/v quantization, and the 4-bit inference performance is 2.4x higher than FP16. The quantization quality has been confirmed via OpenCompass evaluation.

• Effortless Distribution Server: Leveraging the request distribution service, LMDeploy facilitates an easy and efficient deployment of multi-model services across multiple machines and cards.

• Excellent Compatibility: LMDeploy supports KV Cache Quant, AWQ and Automatic Prefix Caching to be used simultaneously.

LMDeploy has developed two inference engines - TurboMind and PyTorch, each with a different focus. The former strives for ultimate optimization of inference performance, while the latter, developed purely in Python, aims to decrease the barriers for developers.

They differ in the types of supported models and the inference data type. Please refer to this table for each engine's capability and choose the proper one that best fits your actual needs.

It is recommended to install lmdeploy using pip in a conda environment (python 3.10 - 3.13):

conda create -n lmdeploy python=3.12 -y
conda activate lmdeploy
pip install lmdeploy

Starting from v0.13.0, the default prebuilt wheels published on PyPI are built against CUDA 12.8, so pip install lmdeploy is sufficient for typical setups including GeForce RTX 50 series.

import lmdeploy
with lmdeploy.pipeline("internlm/internlm3-8b-instruct") as pipe:
    response = pipe(["Hi, pls intro yourself", "Shanghai is"])
    print(response)

For more information about inference pipeline, please refer to here.

Please review getting_started section for the basic usage of LMDeploy.

For detailed user guides and advanced guides, please refer to our tutorials:

• User Guide
  • LLM Inference pipeline
  • VLM Inference pipeline
  • LLM Serving
  • VLM Serving
  • Quantization
• Advanced Guide
  • Inference Engine - TurboMind
  • Inference Engine - PyTorch
  • Customize chat templates
  • Add a new model
  • gemm tuning
  • Long context inference
  • Multi-model inference service

• Deploying LLMs offline on the NVIDIA Jetson platform by LMDeploy: LMDeploy-Jetson

• Example project for deploying LLMs using LMDeploy and BentoML: BentoLMDeploy

We appreciate all contributions to LMDeploy. Please refer to CONTRIBUTING.md for the contributing guideline.

• FasterTransformer
• llm-awq
• vLLM
• DeepSpeed-MII

@misc{2023lmdeploy,
    title={LMDeploy: A Toolkit for Compressing, Deploying, and Serving LLM},
    author={LMDeploy Contributors},
    howpublished = {\url{https://github.com/InternLM/lmdeploy}},
    year={2023}
}

@article{zhang2025lmdeploy,
  title={LMDeploy Accelerates Mixed-Precision LLM Inference with TurboMind},
  author={Zhang, Li and Jiang, Youhe and He, Guoliang and Chen, Xin and Lv, Han and Yao, Qian and Ma, Ningsheng and Fu, Fangcheng and Chen, Kai},
  journal={arXiv preprint arXiv:2508.15601},
  year={2025}
}

This project is released under the Apache 2.0 license.