How to use Magnitude: what you can do with it

3 use cases

• Run local open models faster on your own hardware

  Install Magnitude so each model's kernels are tuned for your chip when you download it.

  • REPO magnitudedev/magnitude
  • Software and DevOps
  • Beginner
  View prompt

  Install magnitudedev/magnitude on this <machine>, download <model>, and compare tokens per second against my current <Ollama or LM Studio> setup on the same prompt.

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• Connect Pi, OpenCode, Hermes or Codex to a local Magnitude model

  Use Magnitude's one-click agent connections to run your coding agent on a fast local model.

  • REPO magnitudedev/magnitude
  • Codex
  • Software and DevOps
  • Beginner
  View prompt

  Connect <Pi or OpenCode or Hermes or Codex> to magnitudedev/magnitude with the best coding model my machine can run, then run a small task in <repo> to check speed and quality.

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• Serve models from a home server that starts at boot

  Run Magnitude server setup so the engine runs as a self-updating service on Linux or macOS.

  • REPO magnitudedev/magnitude
  • Software and DevOps
  • Intermediate
  View prompt

  Set up magnitudedev/magnitude as a boot service with magnitude server setup on <server>, download <models>, and confirm it keeps running after I log out.

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What people are building with Magnitude

• Benchmarkthe web

  Benchmarked Magnitude against llama.cpp, Ollama, uzu and Locals on an M1 Max with the same Qwen3.5 9B file

  With speculative decoding off, Magnitude 0.2.12 generated about 41.6 tok/s on short text and code, roughly 1.12x to 1.14x llama.cpp at about 37 tok/s; on long contexts it held near 40 tok/s against 31 to 34. DFlash drafting helped most on code and cost 7 to 10 percent on long contexts.

  Magnitude on GitHub

  By Ikesan (lilting.ch) on the web·Oct 27, 2026

  View original post on the web

• BenchmarkX

  Timed Magnitude against Ollama on a base M5 MacBook Pro: decode 60 vs 22.6 tok/s

  Same Qwen3.5 4B at 4-bit and the same 9.2k token prompt, with Ollama on its MTP build so both used speculative decoding: prefill 755 vs 464 tok/s, decode 60.0 vs 22.6 tok/s, and Magnitude finished end to end in half the time.

  Magnitude on GitHub

  By Akshay (@akshay_pachaar)·Oct 28, 2026

  View original post on X

Real builds shared on X, Hacker News, Reddit, DEV and GitHub, summed up by Deduped from each post and its links; each card links to the original post. See what people are building with other repos

Quick start with Magnitude

1. Check you have what it needs: macOS, Windows or Linux (x64 or ARM64); Apple Silicon, NVIDIA, AMD or Intel GPU, or a CPU only; Enough memory for the model you choose.
2. Open magnitudedev/magnitude on GitHub and read the README and license.
3. Install with curl -fsSL https://magnitude.dev/install.sh | sh, or the Windows PowerShell or desktop installer
4. Open the app or CLI and download a model; tuning happens on download
5. Run the same prompt you used in Ollama and compare speed
6. Nothing leaves your machine and there are no token costs

These steps come from our top use case, Run local open models faster on your own hardware, which also has a copy-paste agent prompt.

Using the Deduped MCP, get the setup steps for magnitudedev/magnitude

claude mcp add --transport http deduped https://dedupedai.com/mcp

Best AI repos for Software and DevOps

About this repo

• Who it is forPeople running open models locally who want more speed from the same Mac or GPU, especially for coding agents.
• Why it is hot7.5k stars, talked about in 3 AI sources we track
• LanguageRust
• Sourcegithub.com/magnitudedev/magnitude · Back to Hot repos

Requirements

• macOS, Windows or Linux (x64 or ARM64)
• Apple Silicon, NVIDIA, AMD or Intel GPU, or a CPU only
• Enough memory for the model you choose

Related repos

• Trended earlier REPO Niko1221/Strata Strata Strata runs large open models such as Qwen3.8-Flash-Next, Qwen3.6-35B-A3B and DeepSeekV4.1-Flash on your own computer, as a desktop app or a terminal app, with no account. It picks quantized model packs that fit your hardware, reads what does not fit in memory from your SSD, and exposes OpenAI- and Anthropic-compatible APIs so Claude Code, Codex CLI, OpenCode and other tools can use the local model.
• Trended earlier REPO L-Forster/open-jet Open Jet OpenJet is a local AI coding agent and LLM inference engine that runs on your own GPU with no account or API key. `openjet setup` measures your hardware, picks and downloads a model and builds llama.cpp, and on RTX 30, 40 and 50 cards compiles its own CUDA megakernel for Qwen3.8-27B. It can serve 4 agents on one GPU, pair a frontier planner with a local worker (Slipstream), use Ollama or LM Studio, and ship a local model in your app via a Python SDK.
• Trended earlier REPO ollama/ollama Ollama Ollama runs open models such as Gemma, Qwen, DeepSeek, Kimi and gpt-oss locally or in the cloud, with a CLI, a local REST API and official Python and JavaScript libraries.