Magnitude

A closer look at Magnitude

Magnitude is an open source inference engine that tunes its kernels on the user's own hardware to run open models as fast as possible: it ships as a desktop app and a CLI, connects to agents such as Pi, OpenCode, Hermes, Codex, and Claude Code, and keeps prompts, files, and models on the local machine.

Editorial guidance based on published documentation. The workflow below is a suggested starting point, not a measured product result.

When to choose Magnitude

• Developers who run open models locally and want the engine tuned for their exact chip, on Apple Silicon, NVIDIA, AMD, Intel, or CPU only.
• Agent users who want one-click connections from a local engine to Pi, OpenCode, Hermes, Codex, Claude Code, or Cline, or any other agent through the OpenAI-compatible API.
• Users who want private local inference, since the project states nothing leaves the machine once a model is downloaded.

When to choose something else

• Users who want a hosted model API with no local install, since Magnitude is a desktop app and CLI that runs models on the user's own machine.
• Users who need a fixed minimum hardware guarantee, since the project states there is no fixed minimum and smaller machines run smaller models.
• Users who want a managed cloud service with support and hosting, since this is an open source engine run locally, not a hosted product.

A practical starting workflow

1. Install Magnitude from the terminal or download the installer for macOS, Windows, or Linux.
2. Download an open model; Magnitude tunes its kernels on the actual chip when the model is downloaded and keeps the fastest combination.
3. Connect the agent already in use with one click, or point any other agent at the OpenAI-compatible API, and run sessions locally.

Check before committing

• The directory URL is the official GitHub repository, so the repository page is cited as the official page for this entry, not a separate vendor homepage.
• Speed claims such as over 2x faster than Ollama are the project's own benchmark statements, not measured results from this review.

Common questions

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