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

llama.cpp and Ollama 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 Ollama?

The choice between llama.cpp and Ollama 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 Ollama?

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

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llama.cpp vs Ollama

Learn how llama.cpp and Ollama 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.

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125,296stars+3,727(+3.1%)

Last 30 days

Last commit
7 hours ago
Repository age
3 years
Version
v0.2.0
License
MIT
Repository
ggml-org/llama.cpp

Ollama

Runs open-source language models locally with a simple setup, plus optional cloud access for larger models when local hardware isn't enough.

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179,260stars+2,397(+1.4%)

Last 30 days

Last commit
1 day ago
Repository age
3 years
Version
v0.32.15
License
MIT
Self-hosted
Yes
Repository
ollama/ollama

Detailed Comparison

Both llama.cpp and Ollama 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 125,296 stars and Ollama having 179,260 stars on GitHub. In terms of developer contributions, llama.cpp has 22,061 forks, indicating strong developer engagement.

llama.cpp wins

Growth Momentum

llama.cpp is growing faster, adding 3,727 stars in the last 30 days (+3.1%) against adding 2,397 stars for Ollama (+1.4%). llama.cpp is the smaller project of the two, so it is closing the gap rather than extending a lead.

Comparable

Development Activity

Both projects show recent activity, with llama.cpp last updated 7 hours ago and Ollama 1 day ago.

Comparable

Technology Stack

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

Comparable

Project Maturity

Both projects started around the same time, with llama.cpp beginning 3 years ago and Ollama 3 years ago.

Comparable

Licensing

Both projects use the MIT license, providing identical terms for usage and distribution.

Comparable

Use Cases & Features

Both tools serve similar use cases in Local Model Runners.

Ollama wins

Hosting & Deployment

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

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