magnitudedev/magnitudePublic

Open source inference engine that tunes its kernels on your hardware to find the fastest way to run each model. 2x faster than Ollama. Works on Apple Silicon, NVIDIA, AMD, or just a CPU.

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

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7.3K
+39 today
Forks
518
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33
Open issues
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RustApache-2.0Created Jun 12, 2026Last push todayLatest release @magnitudedev/[email protected]+279 stars this week+2.2K this month

Quick answers

What is magnitude?
An open-source inference server that seamlessly connects AI coding agents to locally hosted models
What does magnitude do?
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.
Who is magnitude for?
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.
How do I get started with magnitude?
npm i -g @magnitudedev/cli
How popular is magnitude on GitHub?
magnitudedev/magnitude has 7,335 stars and 518 forks on GitHub, and gained 279 stars in the last 7 days.
What license does magnitude use?
magnitudedev/magnitude is released under the Apache-2.0 license.

Star history

since Sep 3, 2026
02K4K6KSep 2026Sep 2026Oct 2026Oct 2026
7.3K stars as of Oct 25, 2026. Measured daily since Sep 3, 2026; GitHub no longer exposes earlier star timestamps.

Update history

3 recorded
  • Oct 23, 2026New release @magnitudedev/[email protected] (previously @magnitudedev/[email protected]).
  • Oct 15, 2026Description changed from "Open source inference engine for agents that optimizes itself for your exact hardware. Compiles and tunes its kernels on" to "Open source inference engine that tunes its kernels on your hardware to find the fastest way to run each model. 2x faste".
  • Oct 12, 2026New release @magnitudedev/[email protected] (previously @magnitudedev/[email protected]).

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1,119 commits in the last yearLessMore

Signals and awards

derived from tracked data
  • Very active

    1,119 commits in 52 weeks

  • Permissive license

    Apache-2.0

  • Continuous integration

    Automated checks passing

  • Repeat trending

    16 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 open models as fast as your hardware allows

Download Magnitude Documentation Discord Follow Magnitude on Twitter GitHub Repo stars

Magnitude is an open source inference engine that tunes its kernels on your hardware to find the fastest way to run each model. It's over 2x faster than Ollama and works on Apple Silicon, NVIDIA, AMD, or just a CPU. One click connects it to the agent you already use (Pi, OpenCode, Hermes, Codex, and more).

Download Magnitude for macOS, Windows, or Linux

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

demo-9-29.mp4

Get started

  1. Download Magnitude, install it, and open the app.
  2. Choose a recommended model in Discover and download it.
  3. Connect your agent in Connections and start using it.

The desktop app includes the magnitude CLI. No separate installation is needed.

Run it on a server

On Linux and macOS, magnitude server setup runs Magnitude as a service that starts at boot, keeps running after logout, updates itself, and serves browsers and agents on other computers. See Remote server.

Why Magnitude?

  • 2x faster than Ollama: on average across popular local models, on Mac and NVIDIA
  • Tuned on your hardware: kernels are tuned for each model when you download it
  • Best drafter built in: each model ships with the fastest speculative decoding drafter
  • Memory that flexes: quantized KV halves memory per agent, freed when agents stop
  • Fast concurrent sessions: sessions share prefix caches to prevent slowdown
  • Works with your agent: one click to connect Pi, OpenCode, Hermes, Codex, and more
  • Free, private, open source: no token costs, nothing leaves your machine, Apache 2.0

Over 2x faster than Ollama

Magnitude vs Ollama, average of 11 popular local models: 1.32x faster prefill and 2.19x faster decode on Mac, 1.34x faster prefill and 2.17x faster decode on NVIDIA

View full benchmarks →

FAQ

What is Magnitude?

An open source inference engine that optimizes each model for your exact hardware. It ships as a desktop app that runs open models and connects them to the agent you already use.

How is it faster than llama.cpp, Ollama, or LM Studio?

They ship one-size-fits-all kernels, because tuning each model for each chip by hand would take forever. Magnitude tunes them automatically. When you download a model, it spends up to a minute on your actual chip trying different ways to split up the math (how many GPU threads team up, how big a chunk each one takes, how far ahead to load data) and keeps the fastest combination that doesn't change the model's output. See the full benchmarks against Ollama.

What hardware do I need?

Any Apple Silicon, NVIDIA, or AMD GPU, or nothing but a CPU. There is no fixed minimum. Smaller machines run smaller models, and more memory lets you run larger ones.

What operating systems does it support?

macOS, Linux, and Windows.

Which models does it support?

See the full list at magnitude.dev/models. We write optimized kernels for the most popular open-weight families, which is how we beat generalist engines.

Which agents work with it?

One click connects Pi, OpenCode, Hermes, OpenClaw, Codex, Claude Code, Oh My Pi, and Cline. Anything else works through the OpenAI-compatible API.

Is it private?

Yes. Prompts, files, and models stay on your machine. No internet needed once a model is downloaded.

View on GitHub

Recent activity

commits and pull requests

Recent open issues

view all

Discussions

all 3

Releases and announcements

103 total
  1. @magnitudedev/[email protected]@magnitudedev/[email protected]Oct 20, 20265.1K downloads

    ## 0.2.10 ### Patch Changes - [`15ff695`](https://github.com/magnitudedev/magnitude/commit/15ff69513ec4ce5a2420cbdaf5bde9657493f8b4) Thanks [@anerli](https://github.com/anerli)! - Fix memory planning on unified-memory and integrated GPUs. - On NVIDIA GB10 and Apple Silicon, the one-time optimization counts the memory the loaded model already uses instead of overcommitting it ([#199](https://github.com/magnitudedev/magnitude/issues/199), [#206](https://github.com/magnitudedev/magnitude/issues/206)). - Integrated GPUs such as AMD Strix Halo use their shared memory as well as the dedicated carve-out, instead of stopping when the carve-out is full ([#207](https://github.com/magnitudedev/magnitude/issues/207)). - The optimization retries an allocation the GPU refuses instead of failing. - The app lists each GPU once and shows the backend Magnitude uses for it. - [`7f02906`](https://github.com/magnitudedev/magnitude/commit/7f029060a37ed1f7884db038222dc8f36430a08e) Thanks [@anerli](https://github.com/anerli)! - Support models with IQ2_XXS weights on every backend, and fix models with IQ2_XS weights failing to load on a Mac ([#195](https://github.com/magnitudedev/magnitude/issu

  2. @magnitudedev/[email protected]@magnitudedev/[email protected]Oct 15, 20269.3K downloads

    ## 0.2.9 ### Patch Changes - [`5bbdd73`](https://github.com/magnitudedev/magnitude/commit/5bbdd73809a17be7f4dae2e0aed25eac800eb158) Thanks [@thrgreenwald](https://github.com/thrgreenwald)! - Connect Claude Code without a Claude login. Requests, including those from its built-in subagents, use the loaded local model. If you sign in later, reconnect to use your own login for Claude models. - [`195481a`](https://github.com/magnitudedev/magnitude/commit/195481a154c389cfd4b1c9c00d7c08baf3209408) Thanks [@thrgreenwald](https://github.com/thrgreenwald)! - Clarify Discover's speed-versus-quality preference under "Recommended for your machine", and update the README and documentation to explain hardware-specific kernel tuning and the performance comparison with Ollama. - [`ff42cdb`](https://github.com/magnitudedev/magnitude/commit/ff42cdb2a12d1c33a61bbaff1a97525f81829475) Thanks [@thrgreenwald](https://github.com/thrgreenwald)! - Fix installation, update and reliability problems found in release testing. - Linux packages install on RHEL 9 and other systems with glibc 2.34, and the Ubuntu and Debian package no longer pulls a KDE desktop onto headless machines. - The Linux desktop ap

  3. Desktop update acceptance 50.9.1desktop-update-acceptance/0637f5b4ad63ea925d7c6e2208a6a87c65ab4812/50.9.1Oct 15, 2026pre-release158 downloads

    Isolated updater acceptance artifacts; not a production release

  4. Desktop update acceptance 50.9.0desktop-update-acceptance/0637f5b4ad63ea925d7c6e2208a6a87c65ab4812/50.9.0Oct 15, 2026pre-release99 downloads

    Isolated updater acceptance artifacts; not a production release

  5. Desktop update acceptance 50.8.12desktop-update-acceptance/d8d3eedf0a5b2bfe68525ea1de33687920b770d8/50.8.12Oct 14, 2026pre-release32 downloads

    Isolated updater acceptance artifacts; not a production release

Code frequency

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When work happens

weekday and hour
SunMonTueWedThuFriSat036912151821Sun 0:00 — 7 commitsSun 1:00 — 8 commitsSun 2:00 — 6 commitsSun 3:00 — 7 commitsSun 4:00 — 3 commitsSun 5:00 — 3 commitsSun 6:00 — 0 commitsSun 7:00 — 2 commitsSun 8:00 — 2 commitsSun 9:00 — 0 commitsSun 10:00 — 27 commitsSun 11:00 — 5 commitsSun 12:00 — 5 commitsSun 13:00 — 7 commitsSun 14:00 — 6 commitsSun 15:00 — 13 commitsSun 16:00 — 11 commitsSun 17:00 — 8 commitsSun 18:00 — 3 commitsSun 19:00 — 4 commitsSun 20:00 — 10 commitsSun 21:00 — 12 commitsSun 22:00 — 1 commitsSun 23:00 — 2 commitsMon 0:00 — 9 commitsMon 1:00 — 3 commitsMon 2:00 — 2 commitsMon 3:00 — 2 commitsMon 4:00 — 2 commitsMon 5:00 — 0 commitsMon 6:00 — 0 commitsMon 7:00 — 2 commitsMon 8:00 — 3 commitsMon 9:00 — 2 commitsMon 10:00 — 11 commitsMon 11:00 — 9 commitsMon 12:00 — 8 commitsMon 13:00 — 15 commitsMon 14:00 — 16 commitsMon 15:00 — 12 commitsMon 16:00 — 25 commitsMon 17:00 — 20 commitsMon 18:00 — 16 commitsMon 19:00 — 17 commitsMon 20:00 — 16 commitsMon 21:00 — 8 commitsMon 22:00 — 4 commitsMon 23:00 — 12 commitsTue 0:00 — 8 commitsTue 1:00 — 10 commitsTue 2:00 — 6 commitsTue 3:00 — 8 commitsTue 4:00 — 10 commitsTue 5:00 — 13 commitsTue 6:00 — 10 commitsTue 7:00 — 13 commitsTue 8:00 — 11 commitsTue 9:00 — 21 commitsTue 10:00 — 8 commitsTue 11:00 — 13 commitsTue 12:00 — 7 commitsTue 13:00 — 10 commitsTue 14:00 — 12 commitsTue 15:00 — 17 commitsTue 16:00 — 8 commitsTue 17:00 — 17 commitsTue 18:00 — 14 commitsTue 19:00 — 22 commitsTue 20:00 — 8 commitsTue 21:00 — 13 commitsTue 22:00 — 20 commitsTue 23:00 — 8 commitsWed 0:00 — 4 commitsWed 1:00 — 14 commitsWed 2:00 — 6 commitsWed 3:00 — 4 commitsWed 4:00 — 7 commitsWed 5:00 — 1 commitsWed 6:00 — 3 commitsWed 7:00 — 3 commitsWed 8:00 — 1 commitsWed 9:00 — 7 commitsWed 10:00 — 12 commitsWed 11:00 — 24 commitsWed 12:00 — 15 commitsWed 13:00 — 15 commitsWed 14:00 — 17 commitsWed 15:00 — 17 commitsWed 16:00 — 23 commitsWed 17:00 — 12 commitsWed 18:00 — 10 commitsWed 19:00 — 12 commitsWed 20:00 — 5 commitsWed 21:00 — 15 commitsWed 22:00 — 12 commitsWed 23:00 — 13 commitsThu 0:00 — 7 commitsThu 1:00 — 4 commitsThu 2:00 — 3 commitsThu 3:00 — 1 commitsThu 4:00 — 2 commitsThu 5:00 — 2 commitsThu 6:00 — 2 commitsThu 7:00 — 1 commitsThu 8:00 — 1 commitsThu 9:00 — 2 commitsThu 10:00 — 9 commitsThu 11:00 — 9 commitsThu 12:00 — 9 commitsThu 13:00 — 17 commitsThu 14:00 — 17 commitsThu 15:00 — 15 commitsThu 16:00 — 18 commitsThu 17:00 — 15 commitsThu 18:00 — 10 commitsThu 19:00 — 5 commitsThu 20:00 — 12 commitsThu 21:00 — 15 commitsThu 22:00 — 13 commitsThu 23:00 — 4 commitsFri 0:00 — 8 commitsFri 1:00 — 10 commitsFri 2:00 — 2 commitsFri 3:00 — 4 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 — 8 commitsFri 12:00 — 8 commitsFri 13:00 — 11 commitsFri 14:00 — 8 commitsFri 15:00 — 7 commitsFri 16:00 — 13 commitsFri 17:00 — 21 commitsFri 18:00 — 11 commitsFri 19:00 — 15 commitsFri 20:00 — 9 commitsFri 21:00 — 11 commitsFri 22:00 — 7 commitsFri 23:00 — 10 commitsSat 0:00 — 7 commitsSat 1:00 — 2 commitsSat 2:00 — 6 commitsSat 3:00 — 3 commitsSat 4:00 — 4 commitsSat 5:00 — 2 commitsSat 6:00 — 5 commitsSat 7:00 — 2 commitsSat 8:00 — 2 commitsSat 9:00 — 2 commitsSat 10:00 — 4 commitsSat 11:00 — 15 commitsSat 12:00 — 20 commitsSat 13:00 — 4 commitsSat 14:00 — 10 commitsSat 15:00 — 10 commitsSat 16:00 — 17 commitsSat 17:00 — 6 commitsSat 18:00 — 12 commitsSat 19:00 — 6 commitsSat 20:00 — 2 commitsSat 21:00 — 8 commitsSat 22:00 — 8 commitsSat 23:00 — 5 commits
Commit volume by weekday and hour (UTC). Larger dots mean more commits.
DateListRankStars gained
Sep 29, 2026monthly#8+3,743
Sep 28, 2026monthly#8+3,743
Sep 27, 2026monthly#8+3,675
Sep 26, 2026monthly#8+3,598
Sep 25, 2026monthly#9+3,518
Sep 24, 2026monthly#10+3,430
Sep 23, 2026monthly#9+3,360
Sep 22, 2026monthly#12+3,303
Sep 21, 2026monthly#12+3,298
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
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