Ollama is a command-line tool and local server that manages open-weight language models on the machine it runs on. It ships a CLI, a REST API on port 11434, and client libraries for Python and JavaScript, so the same model can be driven from a terminal, a script, or an existing agent framework. Model execution is handled by the llama.cpp backend, and the project distributes installers for macOS, Windows and Linux alongside an official `ollama/ollama` Docker image.
MIT · Go · LM Studio vs Ollama →
llama.cpp is a C/C++ library and set of command-line tools for running large language models (LLMs) and vision-language models (VLMs) locally. It enables inference without external dependencies, targeting diverse hardware including Apple Silicon, x86 CPUs, NVIDIA GPUs (via CUDA), AMD GPUs (via HIP), and other accelerators. The project lives in the ggml ecosystem, leveraging the ggml tensor computation library for low-level operations and quantized model execution.
MIT · C++ · LM Studio vs llama.cpp →
GPT4All is an open-source application for running large language models locally. It ships as native installers for Windows, Windows ARM, macOS, and Ubuntu, with a community-maintained Flathub package, and it also provides a Python client that wraps llama.cpp implementations under the package name gpt4all. Models are distributed in the GGUF format; the README's own example loads Meta-Llama-3-8B-Instruct.Q4_0.gguf, and the project advertises support for DeepSeek R1 distillations. The codebase is written in C++, licensed MIT, and described as open-source and available for commercial use.
MIT · C++ · LM Studio vs GPT4All →