LlamaGPT vs LocalAI
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
A self-hosted chatbot that runs Llama 2 models locally, so your chats stay on your machine.
Open source · MIT
Open-source engine that runs AI models (text, vision, voice, image, video) locally behind an OpenAI-style API.
The short answer
Choose LlamaGPT if you need…
- People who want a private chatbot and don't want to send prompts to a cloud provider
- Users with a home server running umbrelOS who want a one-click install
- Developers who want a local, OpenAI-style API for testing
- Mac users with Docker who want to try local models
Choose LocalAI if you need…
- Developers who want an OpenAI-compatible endpoint running on their own machines or servers
- Teams that need privacy and want data to stay inside their infrastructure
- People experimenting with several model types (text, voice, image) behind one API
- Users with mixed hardware, such as NVIDIA, AMD, Intel, Apple Silicon or CPU-only machines
Side-by-side
| LlamaGPT | LocalAI | |
|---|---|---|
| Type | App | App |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 10.9k | 50.1k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Clone and build from source | Run with Docker |
| Main language | TypeScript | Go |
| Last code update | 3 years ago | 4 days ago |
What each one is
LlamaGPT
LlamaGPT is a ChatGPT-style chat interface that runs open Llama 2 and Code Llama models on your own hardware instead of a cloud service. The project says no data leaves your device. It is packaged with Docker, so you start it with a script and then use it in a browser at localhost:3000.
LocalAI
LocalAI is a self-hosted server that runs many kinds of AI models on your own hardware, including language, vision, voice, image and video models. It exposes an API that is compatible with OpenAI, Anthropic and ElevenLabs, so existing tools can point at it instead of a cloud service. The README says it can run without a GPU and keeps data on your own infrastructure.
Best for
LlamaGPT
- People who want a private chatbot and don't want to send prompts to a cloud provider
- Users with a home server running umbrelOS who want a one-click install
- Developers who want a local, OpenAI-style API for testing
- Mac users with Docker who want to try local models
LocalAI
- Developers who want an OpenAI-compatible endpoint running on their own machines or servers
- Teams that need privacy and want data to stay inside their infrastructure
- People experimenting with several model types (text, voice, image) behind one API
- Users with mixed hardware, such as NVIDIA, AMD, Intel, Apple Silicon or CPU-only machines
Strengths
LlamaGPT
- Runs locally with no data sent out, as the README states
- Supports several model sizes, from 7B to 70B, plus Code Llama variants
- Offers an OpenAI-compatible API and one-command Docker setup
LocalAI
- Broad model support across text, vision, voice, image and video, per the README
- Works with several hardware types, including CPU-only setups
- API compatibility with OpenAI, Anthropic and ElevenLabs reduces code changes
Not ideal for
LlamaGPT
- People with limited RAM or no capable CPU or GPU, since the larger models need 6GB to over 40GB of memory per the README table and run slowly on small devices
- Anyone who needs the latest hosted models or top-quality answers, because these are smaller open models and the README does not compare their quality to GPT-4
- Users who need to load their own custom models today, since the README lists that feature as not yet done
LocalAI
- People who only want a simple desktop chat app; the README focuses on a server and API, though a macOS launcher exists
- Users with very limited disk space, since models and backends are downloaded on demand and can be large
- Those who need a fully managed cloud service with no setup work
Trade-offs
LlamaGPT
- Speed varies a lot by hardware; the README benchmarks range from 0.9 to 54 tokens per second
- Large models need a lot of memory and disk space
- Custom models and switching models in the UI are still on the roadmap
LocalAI
- Many features and backends make the project complex to configure; the README is long and the docs are external
- Backends are downloaded on demand, so first runs may take time and bandwidth
- The macOS DMG is not signed by Apple, requiring a manual quarantine-removal step
Getting started
LlamaGPT
Clone the repository
git clone https://github.com/getumbrel/llama-gpt.gitEnter the project folder
cd llama-gptLocalAI
Run CPU-only container
docker run -ti --name local-ai -p 8080:8080 localai/localai:latestRun with NVIDIA GPU (CUDA 12)
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-gpu-nvidia-cuda-12Frequently asked questions
What is the difference between LlamaGPT and LocalAI?
LlamaGPT: A self-hosted chatbot that runs Llama 2 models locally, so your chats stay on your machine. LocalAI: Open-source engine that runs AI models (text, vision, voice, image, video) locally behind an OpenAI-style API. LlamaGPT suits people who want a private chatbot and don't want to send prompts to a cloud provider, while LocalAI suits developers who want an openai-compatible endpoint running on their own machines or servers.
Are LlamaGPT and LocalAI free?
LlamaGPT: Free and open source. LocalAI: Free and open source.
Is LlamaGPT or LocalAI more popular?
LocalAI has more GitHub stars (50.1k) than LlamaGPT (10.9k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to LlamaGPT and LocalAI?
Other options people compare them with include Ollama, GPT4All, llama.cpp, DB-GPT, OpenHands.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.