Learn how GPT4All and llama.cpp 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
Repository age
Version
License
Repository

Last 30 days
Last commit
Repository age
Version
License
Repository

llama.cpp appears to have several advantages over GPT4All, particularly in popularity, growth and activity. Consider your specific needs regarding popularity, growth, activity, technology, maturity, licensing and features when making your decision.
llama.cpp leads in popularity with 125,499 stars vs 77,397 stars for GPT4All. The 62% higher star count indicates stronger community adoption. In terms of developer contributions, llama.cpp has 22,129 forks, indicating strong developer engagement.
llama.cpp is growing faster, adding 3,846 stars in the last 30 days (+3.2%) against losing 5 stars for GPT4All (0%). llama.cpp is both larger and pulling further ahead.
llama.cpp shows more recent development activity with its last commit 3 hours ago, while GPT4All was last updated 1 year ago. This suggests llama.cpp is being more actively maintained.
Both tools share common technology foundations, being built with JavaScript, CSS, Bash, Typescript, Python, C, Objective-C, C++. However, they differ in their additional technology choices: llama.cpp leverages SCSS, Swift, Kotlin, MATLAB, SvelteKit, GLSL.
Both projects started around the same time, with GPT4All beginning 3 years ago and llama.cpp 3 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.