magnitudedev/magnitudePublic

Open source inference server for Apple silicon. Profiles your Mac, recommends the best models for it, then downloads, tunes, and runs them. Plugs into Pi, OpenCode, Hermes, OpenClaw, Codex, Claude Code, Oh My Pi, and Cline.

AI summary: An open-source inference server that seamlessly connects AI coding agents to locally hosted models

Stars
4.2K
+110 today
Forks
307
Watchers
20
Open issues
12
Open PRs
1
Contributors
~5
Commits
594
Branches
13

TypeScriptApache-2.0Created Jun 12, 2026Last push 1d agoLatest release @magnitudedev/cli@0.0.11+2.3K stars this week+2.3K this month

Star history

since Mar 8, 2026
02K4KMar 2026May 2026Jul 2026Sep 2026
4.2K stars as of Sep 9, 2026, tracked back to Mar 8, 2026. Historical curve reconstructed from public GitHub event archives, calibrated to the current total.

Contribution activity

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540 commits in the last yearLessMore

Signals and awards

derived from tracked data
  • Breakout launch

    4,225 stars in 91 days

  • Rising fast

    +2,316 stars this week

  • Very active

    540 commits in 52 weeks

  • Permissive license

    Apache-2.0

  • Continuous integration

    Automated checks passing

  • Repeat trending

    7 trending appearances

What magnitude does

Magnitude is a localized inference server designed to bridge the gap between popular AI coding agents (like Claude Code, OpenCode, and Pi) and locally hosted language models. It acts as a smart middleware that profiles the user's host hardware, recommends the optimal models that fit within local memory constraints, and automatically handles downloading and tuning. Once running, it exposes a standardized endpoint that existing agent software can plug into seamlessly. This enables developers to use powerful, automated agent tools entirely offline and privately, without relying on costly or privacy-invasive cloud APIs.

Magnitude is built for software engineers, security-conscious organizations, and AI enthusiasts who want to leverage modern AI coding agents using their own hardware. It is ideal for users with capable local machines who prioritize data privacy and zero-cost inference.

  • Hardware profiling: automatically analyzes system resources to recommend models that fit within available RAM and VRAM.
  • Seamless agent integration: provides drop-in compatibility with popular coding agents like Claude Code, OpenClaw, and Hermes.
  • Automated model management: handles the downloading, tuning, and execution of local models without manual configuration.
  • Offline execution: ensures all inference and data processing stays entirely on the local machine for maximum privacy.
  • Standardized API endpoint: exposes a uniform interface that mimics cloud providers, allowing existing tools to connect without modification.

Where teams use it

Private Code Generation

Allows enterprise developers to use AI coding assistants on proprietary codebases without sending sensitive data to external cloud providers.

Offline Development

Enables engineers to utilize powerful AI agents while working on air-gapped systems or during travel without internet access.

Cost Reduction

Helps heavy users of AI coding tools avoid mounting API costs by shifting inference workloads to their local hardware.

Local Model Experimentation

Provides a streamlined way for developers to test how different open-source models perform with their preferred agent workflows.

Getting started: npm i -g @magnitudedev/cli

README

main branch

Magnitude icon

Magnitude

Run your agent on local models. Free, private, and offline.

Documentation Discord Follow Magnitude on Twitter GitHub Repo stars npm version

Magnitude is an open source inference server that runs the best local models for your hardware, plugged into the agent you already use. It profiles your machine, recommends the models that fit, then downloads, tunes, and runs them. Works with Pi, OpenCode, Hermes, OpenClaw, Codex, Claude Code, Oh My Pi, and Cline, or use the built-in harness.

⭐ Help us reach more developers and grow the Magnitude community. Star this repo!

Pi, OpenCode, Hermes, Codex, Claude Code, and OpenClaw connected to Magnitude, which runs local models for your hardware.

Get started

Send this to your agent to walk through models and setup:

Set up local models for me with the Magnitude CLI. Install it with `npm i -g @magnitudedev/cli` (or my package manager), then run `magnitude docs onboarding` and follow the instructions.

Your agent will profile your hardware, walk you through the best local models for it, download the ones you pick, and switch itself over to them.

Magnitude supports macOS and Linux. Windows is supported through WSL.

Want to browse the models directly?
npm i -g @magnitudedev/cli
magnitude setup

The interactive setup lets you browse the recommended models and choose one yourself.

Why Magnitude?

  • Free to run: no token costs, API keys, or rate limits
  • Fully private and offline: models, prompts, and files stay on your machine
  • Agent-first setup: one prompt and your agent walks you through the rest
  • Knows your hardware: profiles your chip, memory, and bandwidth
  • Recommends what fits: the best models for your machine, with estimated tok/s
  • Tuned end to end: speculative decoding, concurrency, all set for your machine
  • Models on demand: loaded on request, unloaded when idle or memory fills
  • Open source: Apache 2.0, yours to modify

FAQ

What is Magnitude?

An open source inference server that runs the best local models for your hardware, plugged into the agent you already use. It profiles your machine, recommends the models that fit, then downloads, tunes, and runs them.

What hardware do I need?

There's no fixed minimum. Magnitude profiles your hardware and recommends the best models for your machine. More memory lets you run larger models.

Why not just have my agent set up Ollama?

Your agent would be guessing. It doesn't know your hardware, which quant fits, or how fast it'll run. Magnitude gives it a catalog with recommendations computed for your machine, an onboarding flow that writes your harness config, and inference built for agent workloads. Models load just in time and unload when idle or memory gets tight.

Which harnesses work with it?

Pi, OpenCode, Hermes, OpenClaw, Codex, Claude Code, Oh My Pi, and Cline. During setup, your agent connects your harness to the model you pick. Or use Magnitude's built-in harness.

Do I need to manage Magnitude after setup?

No. It runs in the background, loads models when your agent needs them, and unloads them when idle or memory gets tight. Your agent can install or switch models through the Magnitude CLI anytime.

Does my data go to the cloud?

No. Prompts, files, and models stay on your machine.

Can it run completely offline?

Yes. Once Magnitude and a model are downloaded, no internet connection needed.

Can I use models outside the catalog?

Yes. You can download compatible GGUF models from Hugging Face and use them in Magnitude.

Learn more

License

Magnitude is licensed under the Apache License 2.0.

View on GitHub

Recent activity

commits and pull requests

Releases and announcements

45 total
  1. @magnitudedev/cli@0.0.11@magnitudedev/cli@0.0.11Sep 2, 2026645 downloads

    ## 0.0.11 ### Patch Changes - [`a18e40a`](https://github.com/magnitudedev/magnitude/commit/a18e40a26c75ae30e005ea6b2a87d70208bfcebf) Thanks [@anerli](https://github.com/anerli)! - More robust service health checks - [`a18e40a`](https://github.com/magnitudedev/magnitude/commit/a18e40a26c75ae30e005ea6b2a87d70208bfcebf) Thanks [@anerli](https://github.com/anerli)! - Better inference errors for context length exceeded - [`a18e40a`](https://github.com/magnitudedev/magnitude/commit/a18e40a26c75ae30e005ea6b2a87d70208bfcebf) Thanks [@anerli](https://github.com/anerli)! - Improve agent onboarding doc

  2. @magnitudedev/cli@0.0.9@magnitudedev/cli@0.0.9Sep 1, 2026166 downloads

    ## 0.0.9 ### Patch Changes - [`7f0ef1b`](https://github.com/magnitudedev/magnitude/commit/7f0ef1bcc83fc80e2b606b7f48aee85ed7316457) Thanks [@anerli](https://github.com/anerli)! - Load heavy CLI command runtimes only when their command is selected, reducing version and help startup overhead. - [`7759adf`](https://github.com/magnitudedev/magnitude/commit/7759adf540c22141597bd4b7743db6fe310533c8) Thanks [@anerli](https://github.com/anerli)! - Better headless CLI interface

  3. @magnitudedev/cli@0.0.10@magnitudedev/cli@0.0.10Sep 1, 202698 downloads

    ## 0.0.10 ### Patch Changes - [`88a9987`](https://github.com/magnitudedev/magnitude/commit/88a9987267c58ed1633ffa58a896dd3c55dfb6e0) Thanks [@anerli](https://github.com/anerli)! - fix: template inspections failing after model download through CLI - [`88a9987`](https://github.com/magnitudedev/magnitude/commit/88a9987267c58ed1633ffa58a896dd3c55dfb6e0) Thanks [@anerli](https://github.com/anerli)! - fix: CLI commands hanging after completion

  4. @magnitudedev/cli@0.0.9-alpha.0@magnitudedev/cli@0.0.9-alpha.0Aug 31, 2026pre-release7 downloads

    ## 0.0.9-alpha.0 ### Patch Changes - [`7f0ef1b`](https://github.com/magnitudedev/magnitude/commit/7f0ef1bcc83fc80e2b606b7f48aee85ed7316457) Thanks [@anerli](https://github.com/anerli)! - Load heavy CLI command runtimes only when their command is selected, reducing version and help startup overhead.

  5. @magnitudedev/cli@0.0.8@magnitudedev/cli@0.0.8Aug 28, 2026495 downloads

    ## 0.0.8 ### Patch Changes - [`0c82137`](https://github.com/magnitudedev/magnitude/commit/0c82137eb9a4d5042a42accfb3235b0505e479fd) Thanks [@anerli](https://github.com/anerli)! - Add Qwen 3.8 Flash Next to catalog - [`5e46594`](https://github.com/magnitudedev/magnitude/commit/5e4659408029d20ec056fad8c3967a37c553c2fb) Thanks [@thrgreenwald](https://github.com/thrgreenwald)! - Improve onboarding model selection, responsive layout, and mouse interactions - [`a35372a`](https://github.com/magnitudedev/magnitude/commit/a35372aeadac5fd6e0ef1bd574116fe504dab3b8) Thanks [@anerli](https://github.com/anerli)! - DSpark support for LFM 2.5 models - [`1af97f8`](https://github.com/magnitudedev/magnitude/commit/1af97f8df0110295bec46acdc652ed18cc1b05e3) Thanks [@anerli](https://github.com/anerli)! - move setup from --setup flag to setup subcommand - [`2f4ce23`](https://github.com/magnitudedev/magnitude/commit/2f4ce23de9fc75a82b652ff60eb96f2fb2508585) Thanks [@anerli](https://github.com/anerli)! - Connect inference to other harnesses, direct CLI controls, more intuitive onboarding

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540 commits in the last 52 weeks.

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weekday and hour
SunMonTueWedThuFriSat036912151821Sun 0:00 — 3 commitsSun 1:00 — 3 commitsSun 2:00 — 1 commitsSun 3:00 — 0 commitsSun 4:00 — 0 commitsSun 5:00 — 0 commitsSun 6:00 — 0 commitsSun 7:00 — 0 commitsSun 8:00 — 1 commitsSun 9:00 — 0 commitsSun 10:00 — 2 commitsSun 11:00 — 2 commitsSun 12:00 — 2 commitsSun 13:00 — 4 commitsSun 14:00 — 4 commitsSun 15:00 — 6 commitsSun 16:00 — 6 commitsSun 17:00 — 6 commitsSun 18:00 — 2 commitsSun 19:00 — 4 commitsSun 20:00 — 7 commitsSun 21:00 — 2 commitsSun 22:00 — 0 commitsSun 23:00 — 1 commitsMon 0:00 — 3 commitsMon 1:00 — 1 commitsMon 2:00 — 1 commitsMon 3:00 — 1 commitsMon 4:00 — 0 commitsMon 5:00 — 0 commitsMon 6:00 — 0 commitsMon 7:00 — 1 commitsMon 8:00 — 0 commitsMon 9:00 — 1 commitsMon 10:00 — 2 commitsMon 11:00 — 3 commitsMon 12:00 — 4 commitsMon 13:00 — 7 commitsMon 14:00 — 6 commitsMon 15:00 — 4 commitsMon 16:00 — 13 commitsMon 17:00 — 15 commitsMon 18:00 — 6 commitsMon 19:00 — 9 commitsMon 20:00 — 11 commitsMon 21:00 — 3 commitsMon 22:00 — 3 commitsMon 23:00 — 9 commitsTue 0:00 — 1 commitsTue 1:00 — 4 commitsTue 2:00 — 1 commitsTue 3:00 — 0 commitsTue 4:00 — 0 commitsTue 5:00 — 0 commitsTue 6:00 — 1 commitsTue 7:00 — 0 commitsTue 8:00 — 1 commitsTue 9:00 — 0 commitsTue 10:00 — 2 commitsTue 11:00 — 2 commitsTue 12:00 — 2 commitsTue 13:00 — 5 commitsTue 14:00 — 7 commitsTue 15:00 — 10 commitsTue 16:00 — 2 commitsTue 17:00 — 10 commitsTue 18:00 — 11 commitsTue 19:00 — 6 commitsTue 20:00 — 4 commitsTue 21:00 — 3 commitsTue 22:00 — 9 commitsTue 23:00 — 2 commitsWed 0:00 — 0 commitsWed 1:00 — 6 commitsWed 2:00 — 2 commitsWed 3:00 — 1 commitsWed 4:00 — 2 commitsWed 5:00 — 1 commitsWed 6:00 — 0 commitsWed 7:00 — 0 commitsWed 8:00 — 1 commitsWed 9:00 — 2 commitsWed 10:00 — 2 commitsWed 11:00 — 3 commitsWed 12:00 — 11 commitsWed 13:00 — 4 commitsWed 14:00 — 5 commitsWed 15:00 — 9 commitsWed 16:00 — 8 commitsWed 17:00 — 4 commitsWed 18:00 — 4 commitsWed 19:00 — 4 commitsWed 20:00 — 0 commitsWed 21:00 — 5 commitsWed 22:00 — 4 commitsWed 23:00 — 2 commitsThu 0:00 — 2 commitsThu 1:00 — 2 commitsThu 2:00 — 2 commitsThu 3:00 — 0 commitsThu 4:00 — 2 commitsThu 5:00 — 2 commitsThu 6:00 — 1 commitsThu 7:00 — 0 commitsThu 8:00 — 0 commitsThu 9:00 — 0 commitsThu 10:00 — 2 commitsThu 11:00 — 1 commitsThu 12:00 — 4 commitsThu 13:00 — 8 commitsThu 14:00 — 10 commitsThu 15:00 — 9 commitsThu 16:00 — 9 commitsThu 17:00 — 6 commitsThu 18:00 — 7 commitsThu 19:00 — 2 commitsThu 20:00 — 1 commitsThu 21:00 — 5 commitsThu 22:00 — 6 commitsThu 23:00 — 2 commitsFri 0:00 — 4 commitsFri 1:00 — 6 commitsFri 2:00 — 2 commitsFri 3:00 — 1 commitsFri 4:00 — 2 commitsFri 5:00 — 1 commitsFri 6:00 — 0 commitsFri 7:00 — 0 commitsFri 8:00 — 1 commitsFri 9:00 — 4 commitsFri 10:00 — 4 commitsFri 11:00 — 3 commitsFri 12:00 — 5 commitsFri 13:00 — 8 commitsFri 14:00 — 3 commitsFri 15:00 — 4 commitsFri 16:00 — 4 commitsFri 17:00 — 6 commitsFri 18:00 — 0 commitsFri 19:00 — 1 commitsFri 20:00 — 4 commitsFri 21:00 — 0 commitsFri 22:00 — 3 commitsFri 23:00 — 2 commitsSat 0:00 — 2 commitsSat 1:00 — 0 commitsSat 2:00 — 2 commitsSat 3:00 — 1 commitsSat 4:00 — 0 commitsSat 5:00 — 0 commitsSat 6:00 — 0 commitsSat 7:00 — 0 commitsSat 8:00 — 0 commitsSat 9:00 — 1 commitsSat 10:00 — 1 commitsSat 11:00 — 2 commitsSat 12:00 — 10 commitsSat 13:00 — 2 commitsSat 14:00 — 3 commitsSat 15:00 — 7 commitsSat 16:00 — 11 commitsSat 17:00 — 4 commitsSat 18:00 — 6 commitsSat 19:00 — 3 commitsSat 20:00 — 2 commitsSat 21:00 — 4 commitsSat 22:00 — 4 commitsSat 23:00 — 2 commits
Commit volume by weekday and hour (UTC). Larger dots mean more commits.
DateListRankStars gained
Sep 8, 2026weekly#1+1,961
Sep 7, 2026daily#10+674
Sep 7, 2026weekly#1+1,961
Sep 6, 2026daily#10+674
Sep 5, 2026daily#5+391
Sep 4, 2026daily#5+161
Sep 3, 2026daily#10+130
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