Learn how llama.cpp and LocalAI 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.
Last 30 days
Last commit
4 hours agoRepository age
4 yearsVersion
v0.7.0License
MITRepository
ggml-org/llama.cpp
Last 30 days
Last commit
6 hours agoRepository age
4 yearsVersion
v4.12.1License
MITSelf-hosted
YesRepository
mudler/localai
Both llama.cpp and LocalAI 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.
llama.cpp leads in popularity with 132,150 stars vs 49,884 stars for LocalAI. The 165% higher star count indicates stronger community adoption. In terms of developer contributions, llama.cpp has 24,740 forks, indicating strong developer engagement.
llama.cpp is growing faster, adding 2,816 stars in the last 30 days (+2.2%) against adding 642 stars for LocalAI (+1.3%). llama.cpp is both larger and pulling further ahead.
Both projects show recent activity, with llama.cpp last updated 4 hours ago and LocalAI 6 hours ago.
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 LocalAI leverages JSX, Golang, Rust.
Both projects started around the same time, with llama.cpp beginning 4 years ago and LocalAI 4 years ago.
Both projects use the MIT license, providing identical terms for usage and distribution.
Both tools serve similar use cases in Local Model Runners.
LocalAI provides self-hosting options for complete data control and customization, while llama.cpp may be primarily cloud-based or require different deployment approaches.