What is the main difference between llama.cpp and vLLM?

llama.cpp and vLLM are both local model runners but differ in their approach, features, and target audience. Compare their GitHub stats, technology stack, and community adoption to make the best choice.

Which is better: llama.cpp or vLLM?

The choice between llama.cpp and vLLM depends on your specific needs, technical requirements, and team preferences. Both are quality open source solutions with active communities.

Can I migrate from llama.cpp to vLLM?

Migration between llama.cpp and vLLM is possible but requires careful planning. Consider data export/import capabilities, feature parity, and potential workflow changes.

Capture – Capture screenshots, generate PDFs, scrape content, extract metadata, and automate browsers with one API.

Learn More

llama.cpp vs vLLM

Learn how llama.cpp and vLLM differ in their key features, development activity, technology stack and community adoption, so you can decide which of these local model runners is best for you.

llama.cpp

C/C++ inference engine for large language models, supporting quantization, multi-GPU, Apple Silicon, and an OpenAI-compatible server across a wide range of hardware.

Visit llama.cpp Read more

131,176stars+2,968(+2.3%)

Last 30 days

Last commit
3 hours ago
Repository age
4 years
Version
v0.6.0
License
MIT
Repository
ggml-org/llama.cpp

vLLM

Inference and serving engine for large language models, built for speed and hardware efficiency with an OpenAI-compatible API and support for a wide range of open models.

Visit vLLM Read more

93,793stars+1,158(+1.3%)

Last 30 days

Last commit
3 hours ago
Repository age
4 years
Version
v0.31.0
License
Apache-2.0
Self-hosted
Yes
Repository
vllm-project/vllm

Sponsor vLLM ongithub.com

Detailed Comparison

Both llama.cpp and vLLM have their unique strengths and serve similar purposes effectively. Consider your specific needs regarding popularity, growth, activity, technology, maturity, licensing and features when making your decision.

Comparable

Community & Popularity

Both tools have similar popularity levels, with llama.cpp having 131,176 stars and vLLM having 93,793 stars on GitHub. In terms of developer contributions, llama.cpp has 24,408 forks, indicating strong developer engagement.

llama.cpp wins

Growth Momentum

llama.cpp is growing faster, adding 2,968 stars in the last 30 days (+2.3%) against adding 1,158 stars for vLLM (+1.3%). llama.cpp is both larger and pulling further ahead.

Comparable

Development Activity

Both projects show recent activity, with llama.cpp last updated 3 hours ago and vLLM 3 hours ago.

Comparable

Technology Stack

Both tools share common technology foundations, being built with JavaScript, CSS, Bash, Python, C, Objective-C, C++. However, they differ in their additional technology choices: llama.cpp uses Typescript, SCSS, Swift, Kotlin, MATLAB, SvelteKit, GLSL while vLLM leverages Rust.

Comparable

Project Maturity

Both projects started around the same time, with llama.cpp beginning 4 years ago and vLLM 4 years ago.

llama.cpp wins

Licensing

llama.cpp uses the MIT license, which is more permissive than vLLM's Apache-2.0 license, potentially offering greater flexibility for commercial use and integration.

Comparable

Use Cases & Features

Both tools serve similar use cases in Local Model Runners.

vLLM wins

Hosting & Deployment

vLLM provides self-hosting options for complete data control and customization, while llama.cpp may be primarily cloud-based or require different deployment approaches.

Other tool comparisons to consider