LlamaGPT vs Ollama
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
Runs open AI models like Gemma, Qwen and DeepSeek on your own machine or in the cloud.
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 Ollama if you need…
- Developers who want a local model API for their own apps
- People who want to chat with open models without sending prompts to a hosted service
- Users who want to connect a coding assistant to a local or self-chosen model
- Students experimenting with different open models from a terminal
Side-by-side
| LlamaGPT | Ollama | |
|---|---|---|
| Type | App | App |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 10.9k | 186k |
| Setup difficulty | Intermediate | Beginner |
| How you get it | Clone and build from source | Install with a package manager |
| Platforms | — | macOS, Windows, Linux, CLI, API |
| Works with | — | VS Code |
| Main language | TypeScript | Go |
| Last code update | 3 years ago | 5 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.
Ollama
Ollama is a tool for downloading and running open-weight AI language models locally, with a simple command line and a REST API. The README says it also offers cloud access and can connect to coding tools and assistants such as Claude Code, Codex and OpenClaw. People use it to try models privately or to build apps that call a model through a local API.
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
Ollama
- Developers who want a local model API for their own apps
- People who want to chat with open models without sending prompts to a hosted service
- Users who want to connect a coding assistant to a local or self-chosen model
- Students experimenting with different open models from a terminal
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
Ollama
- Simple install and run commands across macOS, Windows, Linux and Docker
- Official Python and JavaScript libraries plus a documented REST API
- Large community with many integrations listed in the README
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
Ollama
- People who need the most capable closed frontier models, since this focuses on open models
- Users with limited disk space or memory, because model files can be large and need capable hardware (the README does not give specific requirements)
- Non-technical users who want a polished desktop app with no terminal use, although the README mentions a download option for macOS and Windows
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
Ollama
- The README is truncated and does not state hardware requirements or model sizes
- Some features, such as cloud access and specific integrations, depend on Ollama's own services and documentation that is only partly described here
- Model quality and licensing depend on each individual model, not on Ollama itself
Getting started
LlamaGPT
Clone the repository
git clone https://github.com/getumbrel/llama-gpt.gitEnter the project folder
cd llama-gptOllama
macOS and Linux install
curl -fsSL https://ollama.com/install.sh | shWindows install (PowerShell)
irm https://ollama.com/install.ps1 | iexFrequently asked questions
What is the difference between LlamaGPT and Ollama?
LlamaGPT: A self-hosted chatbot that runs Llama 2 models locally, so your chats stay on your machine. Ollama: Runs open AI models like Gemma, Qwen and DeepSeek on your own machine or in the cloud. LlamaGPT suits people who want a private chatbot and don't want to send prompts to a cloud provider, while Ollama suits developers who want a local model api for their own apps.
Are LlamaGPT and Ollama free?
LlamaGPT: Free and open source. Ollama: Free and open source.
Which is easier to set up, LlamaGPT or Ollama?
Ollama is easier to get started with (rated beginner); LlamaGPT is rated intermediate and usually needs more setup.
Is LlamaGPT or Ollama more popular?
Ollama has more GitHub stars (186k) 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 Ollama?
Other options people compare them with include GPT4All, llama.cpp, DB-GPT, LocalAI, 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.