GPT4All vs LlamaGPT
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
Desktop app and Python package for running open-source language models locally on your computer.
Open source · MIT
A self-hosted chatbot that runs Llama 2 models locally, so your chats stay on your machine.
The short answer
Choose GPT4All if you need…
- People who want to chat with a language model without sending their text to a cloud service
- Developers who want to call local models from Python
- Users with ordinary laptops who want to try local AI without dedicated graphics hardware
- Anyone who wants to chat with their own documents locally using the LocalDocs feature
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
Side-by-side
| GPT4All | LlamaGPT | |
|---|---|---|
| Type | App | App |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 77.3k | 10.9k |
| Setup difficulty | Beginner | Intermediate |
| How you get it | Download the app | Clone and build from source |
| Main language | C++ | TypeScript |
| Last code update | 18 months ago | 3 years ago |
What each one is
GPT4All
GPT4All is an open-source tool that runs large language models privately on everyday desktops and laptops. According to its README, you can download the application and start without API calls or GPUs being required. It also offers a Python client built around llama.cpp implementations for developers who want to use local models in code.
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.
Best for
GPT4All
- People who want to chat with a language model without sending their text to a cloud service
- Developers who want to call local models from Python
- Users with ordinary laptops who want to try local AI without dedicated graphics hardware
- Anyone who wants to chat with their own documents locally using the LocalDocs feature
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
Strengths
GPT4All
- Runs locally, so prompts are not sent to an API by default according to the README's privacy framing
- Has desktop installers for Windows, macOS, and Linux plus a Python package
- Released under the MIT license and marked as available for commercial use
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
Not ideal for
GPT4All
- Users on very old hardware, since the README lists minimum processor requirements (Intel Core i3 2nd Gen / AMD Bulldozer or better for Windows and Linux)
- Linux users on ARM processors, because the README says the Linux build is x86-64 only
- People who need the most powerful cloud-hosted models, since local models run on your own machine and are limited by its resources
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
Trade-offs
GPT4All
- Output quality and speed depend on your hardware and the model you choose, and the README does not compare them to cloud models
- The README's system requirements are split across platforms and point to a separate document for full details
- Some features and release history details are not explained in depth in the README
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
Getting started
LlamaGPT
Clone the repository
git clone https://github.com/getumbrel/llama-gpt.gitEnter the project folder
cd llama-gptFrequently asked questions
What is the difference between GPT4All and LlamaGPT?
GPT4All: Desktop app and Python package for running open-source language models locally on your computer. LlamaGPT: A self-hosted chatbot that runs Llama 2 models locally, so your chats stay on your machine. GPT4All suits people who want to chat with a language model without sending their text to a cloud service, while LlamaGPT suits people who want a private chatbot and don't want to send prompts to a cloud provider.
Are GPT4All and LlamaGPT free?
GPT4All: Free and open source. LlamaGPT: Free and open source.
Which is easier to set up, GPT4All or LlamaGPT?
GPT4All is easier to get started with (rated beginner); LlamaGPT is rated intermediate and usually needs more setup.
Is GPT4All or LlamaGPT more popular?
GPT4All has more GitHub stars (77.3k) than LlamaGPT (10.9k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to GPT4All and LlamaGPT?
Other options people compare them with include llama.cpp, Ollama, 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.