llama.cpp 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
Runs large language models locally in plain C/C++, on CPUs and many kinds of GPUs.
Open source · MIT
A self-hosted chatbot that runs Llama 2 models locally, so your chats stay on your machine.
The short answer
Choose llama.cpp if you need…
- Developers who want to run open-weight models locally without a cloud API
- People with Apple silicon or other non-NVIDIA hardware who want GPU-accelerated inference
- Teams that want a self-hosted OpenAI-compatible endpoint
- Researchers experimenting with quantized models on limited memory
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
| llama.cpp | LlamaGPT | |
|---|---|---|
| Type | Library | App |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 133k | 10.9k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | See the README | Clone and build from source |
| Main language | C++ | TypeScript |
| Last code update | 4 days ago | 3 years ago |
What each one is
llama.cpp
llama.cpp is an open-source project for running large language models (LLMs), and vision-language models, on your own hardware. It is written in C/C++ with no required dependencies and supports many chip types, including Apple silicon, x86 and RISC-V CPUs, and NVIDIA, AMD and other GPUs. People use it to run models locally or on their own servers, with a command-line tool, a built-in web UI, and an OpenAI-compatible API server.
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
llama.cpp
- Developers who want to run open-weight models locally without a cloud API
- People with Apple silicon or other non-NVIDIA hardware who want GPU-accelerated inference
- Teams that want a self-hosted OpenAI-compatible endpoint
- Researchers experimenting with quantized models on limited memory
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
llama.cpp
- Plain C/C++ with no required dependencies, and a wide range of supported hardware backends
- Quantization options from 1.5-bit to 8-bit reduce memory needs
- Large, active open-source project under the MIT license
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
llama.cpp
- Users who want a polished consumer chat app with no setup; the README points to command-line and build steps
- People who need a managed cloud service, since this is software you run yourself
- Users with very limited RAM or VRAM, since performance depends heavily on hardware and model size
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
llama.cpp
- Setup can require building from source or choosing the right backend for your hardware
- Performance and supported features vary by backend and device, and some backends are marked in progress
- Results depend on local hardware, so the README's performance claims are hard to verify for your machine
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
llama.cpp
Install with curl (macOS/Linux installer from README)
curl -LsSf https://llama.app/install.sh | shInstall with PowerShell (Windows installer from README)
irm https://llama.app/install.ps1 | iexLlamaGPT
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 llama.cpp and LlamaGPT?
llama.cpp: Runs large language models locally in plain C/C++, on CPUs and many kinds of GPUs. LlamaGPT: A self-hosted chatbot that runs Llama 2 models locally, so your chats stay on your machine. llama.cpp suits developers who want to run open-weight models locally without a cloud api, while LlamaGPT suits people who want a private chatbot and don't want to send prompts to a cloud provider.
Are llama.cpp and LlamaGPT free?
llama.cpp: Free and open source. LlamaGPT: Free and open source.
Is llama.cpp or LlamaGPT more popular?
llama.cpp has more GitHub stars (133k) than LlamaGPT (10.9k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to llama.cpp and LlamaGPT?
Other options people compare them with include Ollama, GPT4All, 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.