LocalAI implements the OpenAI API on your own hardware. Applications written against OpenAI work unmodified: change the base URL, keep the code.

Why API compatibility matters more than the models

An enormous amount of software has been written against the OpenAI API. Libraries, frameworks, editor plugins, and internal tools all speak it.

That makes API compatibility a more valuable property than any particular model. LocalAI takes an existing application, changes nothing about it, and moves the inference onto hardware you control.

Broader than chat

Where Ollama focuses on running language models well, LocalAI aims to cover the API surface:

• Chat and text completion
• Embeddings, which is what RAG applications need
• Audio transcription, compatible with the Whisper endpoint
• Text to speech
• Image generation through Stable Diffusion

Running one service that answers all of those is considerably simpler than assembling four.

Backends

LocalAI is a front-end to several inference engines including llama.cpp, so model format support is broad. It runs on CPU, and with a GPU it is dramatically faster.

Models are configured through YAML files describing the backend, the model file, and its parameters. That is more setup than ollama run and it is also what makes it flexible.

Hardware

The same constraint applies as anywhere else: model size determines memory. A 7B model quantized to 4-bit wants roughly 5GB; larger models want proportionally more. CPU inference works and is slow enough to be unpleasant interactively.

Image generation is the heaviest workload and effectively wants a GPU.

Where it fits

Choose LocalAI when you have an application that speaks the OpenAI API and want to keep the data local, or when you want one endpoint covering chat, embeddings, and audio.

Choose Ollama when you mainly want to run language models and value the simpler setup.

License

LocalAI is released under the MIT License.

Links

• Website
• Repository