GraphCanon updated 2w · GitHub synced 2w
Decision brief
FauxPilot is an open-source alternative to GitHub Copilot, which uses NVIDIA's Triton Inference Server with the FasterTransformer backend to serve SalesForce CodeGen models locally.
Good fit when
- You have access to a powerful GPU that meets or exceeds the required Compute Capability and VRAM for running the chosen model.
- You prefer open-source solutions over proprietary ones, such as GitHub Copilot.
Avoid when
- If you lack the necessary hardware with the requisite GPU compute capability and VRAM to support running FauxPilot.
- For users who do not have experience setting up Docker containers, NVIDIA's Triton Inference Server, or FasterTransformer backend as these are required for operation.
- Pricing:
- freemium - FauxPilot is free to use under the MIT License. However, users will need to cover costs associated with running it on local hardware, including setting up Docker and having an NVIDIA GPU.
- Requirements:
- Requires docker, docker-compose version >=1.28, an NVIDIA GPU with Compute Capability >=6.0 and sufficient VRAM for the selected model.; Users need to have `curl` and `zstd` installed on their systems for downloading models.
Observed Jul 17, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Dormant (845d since push)
- As of 2w
- Provenance
- Not a fork · Organization account
- As of 2w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install fauxpilotSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
FauxPilot is an open-source project that serves as an alternative to GitHub Copilot by leveraging NVIDIA's Triton Inference Server with the FasterTransformer backend for serving SalesForce CodeGen models locally.
Capability facts
- Languages
- python
Source: github.language · Aug 2, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 2, 2026)
ct to FauxPilot Server. For example, you can create a client by how to open the Openai API, Copilot Plugin, REST API.Source link
Tags
README
FauxPilot
This is an attempt to build a locally hosted alternative to GitHub Copilot. It uses the SalesForce CodeGen models inside of NVIDIA's Triton Inference Server with the FasterTransformer backend.
Prerequisites
You'll need:
- Docker
docker compose>= 1.28- An NVIDIA GPU with Compute Capability >= 6.0 and enough VRAM to run the model you want.
nvidia-dockercurlandzstdfor downloading and unpacking the models.
Note that the VRAM requirements listed by setup.sh are total -- if you have multiple GPUs, you can split the model across them. So, if you have two NVIDIA RTX 3080 GPUs, you should be able to run the 6B model by putting half on each GPU.
Support and Warranty
lmao
Okay, fine, we now have some minimal information on the wiki and a discussion forum where you can ask questions. Still no formal support or warranty though!
Setup
This section describes how to install a Fauxpilot server and clients.
Setting up a FauxPilot Server
Run the setup script to choose a model to use. This will download the model from Huggingface/Moyix in GPT-J format and then convert it for use with FasterTransformer.
Please refer to How to set-up a FauxPilot server.
Client configuration for FauxPilot
We offer some ways to connect to FauxPilot Server. For example, you can create a client by how to open the Openai API, Copilot Plugin, REST API.
Please refer to How to set-up a client.
Terminology
- API: Application Programming Interface
- CC: Compute Capability
- CUDA: Compute Unified Device Architecture
- FT: Faster Transformer
- JSON: JavaScript Object Notation
- gRPC: Remote Procedure call by Google
- GPT-J: A transformer model trained using Ben Wang's Mesh Transformer JAX
- REST: REpresentational State Transfer
For agents
This page has a .md twin and JSON over the API.