# BitRun (slim) > BitRun is an open-source command-line runner for 1-bit and 1.58-bit ternary language models such as BitNet b1.58, Falcon3 1.58-bit and Bonsai on CPUs, with a model downloader and a local OpenAI-compatible server. - Full: https://www.anchorterminal.com/tools/bitrun.md (~6,150 tokens) · this version ~1,630 tokens · JSON https://www.anchorterminal.com/tools/bitrun.json · canonical https://www.anchorterminal.com/tools/bitrun - Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · Updated: 2026-10-15 - Licence: CC BY 4.0, credit Anchor Terminal (anchorterminal.com) and link this page when you pass anything on **F · 37.9/100 · rank #1018 of 1094 · #18 in Local AI · not agent-ready · confidence medium** Assessment: 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. ## Facts - Kind: HTTP API · vendor: Abhishek Khanra (IshekKhal on GitHub) · category: Local AI · legal entity: Abhishek Khanra (IshekKhal on GitHub) · provenance 53/100 - Local only (HTTP) - Auth: None · pricing: Free · x402: no · licence: MIT - Probe metrics: not measured yet (probes haven't run) - Surface listed: BitRun v0.2.0-beta, its CLI and the local OpenAI-compatible server started by `bitrun serve`, read from the repository at commit 0fec65a, the README and the release notes on 15 October 2026. Nothing was installed or run - Interfaces: CLI commands cockpit, run, bench, pull, serve, list, create and hardware. `serve` starts a patched llama-server on 127.0.0.1:11435. No MCP server and no SDK of its own - Models: bitnet-70m (Q8_0, 79 MB, 768 tokens), bonsai-1.7b (Q1_0, 237 MB, 2,048), falcon3-1b (I2_S, 1.30 GB, 4,096), bitnet-2b (I2_S, 1.13 GB, 2,048), falcon3-3b (I2_S, 2.07 GB, 4,096) and bonsai-8b (PQ2_0, 2.08 GB, 8,192), per the README. Custom personas come from Modelfiles over these - Engine: Statically linked llama-cli, llama-server and llama-quantize from llama.cpp with BitRun's patches for I2_S and Prism group-128 types, ARM NEON dot-product kernels and squared ReLU. Earlier versions patched Microsoft's bitnet.cpp - Platforms: Release archives for macOS arm64, Linux amd64 and Windows amd64. No Intel macOS or Linux arm64 archive - Hardware: CPU only (`-ngl 0`). Threads default to the number of cores less two, capped at eight - Network: Model downloads from huggingface.co on `pull`, and the install scripts fetch from GitHub. No other outbound call found in the Go source - Telemetry: "Telemetry" in the README means local CPU and power readings shown on screen. On macOS it reads battery power through `ioreg` and otherwise estimates, on Linux it reads RAPL counters when present, and on Windows it estimates - Vendor benchmarks: The README reports 72.8 tokens a second generation for falcon3-1b and 31.6 for bitnet-2b on an M4 Pro CPU (vendor figures). The v0.1.x notes gave 98.6 and 49.3 for the same models on the same chip - Releases: v0.1.0-alpha (12 October 2026), v0.1.1-alpha (14 October), v0.2.0-beta (15 October). 13 commits - Show HN: Posted 13 October 2026 at 19:28 UTC, 2 points and no comments when read on 15 October - Scores: Reliability 38, Performance pending, Schema & documentation 30, Agent ergonomics 40, Security & auth 28, Payments & pricing 60, Task success pending, Maintenance & community 59, Transparency & trust 50 · negative events -3 · total over the 7 assessed categories - Why: Reliability, Read with the local-software lines, since BitRun runs on the owner's machine with no hosted service. · Schema & documentation, Read with the API lines, for the local server an agent calls. · Agent ergonomics, Read with the API lines. · Security & auth, Read with the tool checklist, credential model first. · Payments & pricing, Read with the self-hosted rule. · Maintenance & community, v0.2.0-beta on 15 October 2026 (30). · Transparency & trust, The editorial half. - Sources: 15, open questions: 5, both in the full twin - Capabilities: inference.local, inference.open-weights - JSON: https://www.anchorterminal.com/api/v1/tools/bitrun.json - Verify (for the vendor): the badge `https://www.anchorterminal.com/badges/bitrun.svg` or a link to https://www.anchorterminal.com/tools/bitrun from a page under github.com/ishekkhal, or the README of github.com/IshekKhal/bitrun, then `POST https://www.anchorterminal.com/api/v1/verify` `{"slug", "url"}` or `verify_listing` at /mcp; re-checked weekly, no effect on the grade. Snippets in the full twin. ## Before you call it 1. Ask the user to install BitRun and run `bitrun pull ` and `bitrun serve ` 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 ## Connect ```bash curl -fsSL https://raw.githubusercontent.com/IshekKhal/bitrun/main/install.sh | sh bitrun pull falcon3-1b bitrun serve falcon3-1b --port 11435 ``` ```bash curl -s http://127.0.0.1:11435/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{"model": "falcon3-1b", "messages": [{"role": "user", "content": "How do 1-bit models conserve memory bandwidth?"}], "temperature": 0.2}' ``` Full config and headless snippets are in the full page. Through letme (picks today, calling later): https://letme.dev/bitrun ## Similar tools | Tool | Grade | Score | Shared capabilities | Slim | | --- | --- | --- | --- | --- | | LocalAI | B | 68 | inference.local, inference.open-weights | https://www.anchorterminal.com/tools/localai.min.md | | Lemonade | B | 63.8 | inference.local, inference.open-weights | https://www.anchorterminal.com/tools/lemonade.min.md | | Foundry Local | C | 60.5 | inference.local, inference.open-weights | https://www.anchorterminal.com/tools/foundry-local.min.md | | KoboldCpp | C | 60.5 | inference.local, inference.open-weights | https://www.anchorterminal.com/tools/koboldcpp.min.md | | llama.cpp | C | 60.2 | inference.local, inference.open-weights | https://www.anchorterminal.com/tools/llama-cpp.min.md | ## Panel reviews (0, desk reviews from public material, no calls made)