FauxPilot 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 server that aims to stand in for GitHub Copilot, running code models on your own GPU.
Open source · MIT
Runs open AI models like Gemma, Qwen and DeepSeek on your own machine or in the cloud.
The short answer
Choose FauxPilot if you need…
- Developers with an NVIDIA GPU who want to keep their code on their own machines
- Teams experimenting with self-hosted code completion
- Users who already run Docker and are comfortable with server setup
- People who want to connect editors or tools through an OpenAI-style or REST API
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
| FauxPilot | Ollama | |
|---|---|---|
| Type | Dev tool | App |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 14.7k | 186k |
| Setup difficulty | Advanced | 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 | Python | Go |
| Last code update | 3 years ago | 4 days ago |
What each one is
FauxPilot
FauxPilot is an attempt to build a locally hosted alternative to GitHub Copilot. It runs SalesForce CodeGen models on NVIDIA's Triton Inference Server with the FasterTransformer backend, so code suggestions are generated on your own hardware instead of a cloud service. People use it when they want Copilot-style completions without sending code to a third party.
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
FauxPilot
- Developers with an NVIDIA GPU who want to keep their code on their own machines
- Teams experimenting with self-hosted code completion
- Users who already run Docker and are comfortable with server setup
- People who want to connect editors or tools through an OpenAI-style or REST API
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
FauxPilot
- Runs locally, so code does not have to leave your own infrastructure
- Uses established components: Triton Inference Server and FasterTransformer
- Open source under the MIT license with a large following (14,668 stars shown)
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
FauxPilot
- People without an NVIDIA GPU, since the README lists one as a requirement
- Users who want a polished, supported product; the README states there is no formal support or warranty
- Anyone needing a guaranteed-current model lineup, since the last push shown is April 2024
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
FauxPilot
- Requires an NVIDIA GPU with Compute Capability 6.0 or higher and enough VRAM for the chosen model
- Setup involves Docker, nvidia-docker, and model download and conversion steps
- The README gives no formal support, and the last push shown is from April 2024
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
FauxPilot
Clone the repository
git clone https://github.com/fauxpilot/fauxpilotOllama
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 FauxPilot and Ollama?
FauxPilot: A self-hosted server that aims to stand in for GitHub Copilot, running code models on your own GPU. Ollama: Runs open AI models like Gemma, Qwen and DeepSeek on your own machine or in the cloud. FauxPilot suits developers with an nvidia gpu who want to keep their code on their own machines, while Ollama suits developers who want a local model api for their own apps.
Are FauxPilot and Ollama free?
FauxPilot: Free and open source. Ollama: Free and open source.
Which is easier to set up, FauxPilot or Ollama?
Ollama is easier to get started with (rated beginner); FauxPilot is rated advanced and usually needs more setup.
Is FauxPilot or Ollama more popular?
Ollama has more GitHub stars (186k) than FauxPilot (14.7k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to FauxPilot and Ollama?
Other options people compare them with include Tabby, llama.cpp, MindsHub, Reasonix, magpie.
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.