llama.cpp vs LMDeploy

A side-by-side comparison of two self-hosted local llm runners options — licensing, setup difficulty, resource needs, and what each one replaces.

Not the right match-up? Compare llama.cpp with Compare LMDeploy with

llama.cpp High-performance LLM inference in plain C/C++ VS LM LMDeploy Toolkit for compressing and serving large language models

Feature	llama.cpp	LMDeploy
Deploy effort	Under-an-hour setup	Under-an-hour setup
Health score	100 · Excellent	92 · Excellent
Category	Local LLM Runners	Local LLM Runners
License	MIT	Apache-2.0
Language	C++	Python
Setup difficulty	Hard	Hard
Min. RAM	8,192 MB	16,384 MB
Deployment	binary, bare-metal, docker	docker, source
GitHub stars	★ 129,246	★ 8,092
First released	2023	2023
Replaces	OpenAI API	OpenAI API, Hugging Face Inference Endpoints

What are llama.cpp and LMDeploy?

llama.cpp

llama.cpp is a C/C++ inference engine for running LLaMA-family and many other models efficiently on CPUs and GPUs. It pioneered the GGUF quantized model format and powers a large portion of the local-AI ecosystem.

GGUF quantization
Runs on modest hardware
Built-in HTTP server
Broad GPU backend support

Read the full llama.cpp guide →

LMDeploy

LMDeploy is an inference and serving toolkit from the OpenMMLab ecosystem for deploying large language models efficiently. It offers quantization, a high-throughput serving engine, and an OpenAI-compatible API.

High-throughput inference engine
Weight quantization support
OpenAI-compatible serving
Multi-GPU deployment

llama.cpp vs LMDeploy: key differences

Llama.cpp is written in C++, while LMDeploy is built with Python. Licensing differs — MIT for llama.cpp versus Apache-2.0 for LMDeploy. Llama.cpp is the lighter option, starting around 8,192 MB of RAM against 16,384 MB for LMDeploy. Llama.cpp has the considerably larger community, at 129,246 GitHub stars versus 8,092.

Why pick each one

Choose llama.cpp if…

Runs on modest CPUs
Broad hardware support
Pioneered GGUF quantization

Watch out for

Command-line focused
Frequent breaking changes

llama.cpp details

Choose LMDeploy if…

Released under the Apache-2.0 license
First-class Docker support for quick deployment
Mature project with 8.1k GitHub stars
Written in Python

LMDeploy details

Frequently asked questions

Is llama.cpp or LMDeploy better?

Neither is universally better. llama.cpp has the larger community; both share a hard setup difficulty, so the decision comes down to features and licensing.

Are llama.cpp and LMDeploy free and open-source?

Yes. llama.cpp is licensed under MIT and LMDeploy under Apache-2.0. Both can be self-hosted at no software cost.

Can I run llama.cpp and LMDeploy with Docker?

llama.cpp: yes. LMDeploy: yes.

Which is lighter on resources, llama.cpp or LMDeploy?

llama.cpp has the smaller minimum footprint at 8,192 MB of RAM, compared to about 16,384 MB for LMDeploy. Real-world usage depends on library size, user count, and enabled features.