BitRun vs TextGen

BitRun

BitRun packages patched llama.cpp builds for ternary models into one download for macOS on Apple silicon, Windows and Linux, with a loopback-only OpenAI-compatible server. It is three days old with three pre-release versions, one maintainer and no CI, and its published power figures come from a fixed formula rather than measurement.

TextGen

AGPL-3.0 with no telemetry, and a local API on 127.0.0.1:5000 that checks the Host header, limits CORS to localhost and separates an admin key from the caller's key. No release since v4.9 on 20 May 2026, no code commits since 31 May, no test suite, and ten security advisories in the year, all fixed.

BitRun

1. Ask the user to install BitRun and run bitrun pull <tag> and bitrun serve <tag> first. Then call http://127.0.0.1:11435/v1 with any API key string
2. Poll /health before the first request. The server loads the model in the background and answers only when ready
3. Use a model tag from the catalogue (bitnet-70m, bonsai-1.7b, falcon3-1b, bitnet-2b, falcon3-3b, bonsai-8b) and keep prompts within its context window
4. Treat the README's energy figures as estimates, and don't report them as measured power
5. Expect small research checkpoints. Check outputs before relying on them for code or facts

TextGen

1. Ask the owner to launch with --api. Nothing listens on port 5000 without it, and a model must be loaded first
2. Call http://127.0.0.1:5000/v1. Any Host header other than localhost or 127.0.0.1 gets 400 Invalid host header unless --listen is set
3. Send the key as Authorization: Bearer on OpenAI routes and as x-api-key on /v1/messages. Model loading needs the admin key
4. Read http://127.0.0.1:5000/docs or modules/api/typing.py for parameters. max_tokens defaults to 512 on chat completions
5. Run tool calls yourself. The API returns finish_reason: "tool_calls" and executes nothing on the server
6. Use the repository name oobabooga/textgen. The old text-generation-webui URL redirects