magnitudedev/magnitude

Open source inference server that runs the best local models for your hardware, plugged into the agent you already use. Works with Pi, OpenCode, Hermes,...

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Summary Information

Updated 52 minutes ago
Added to GitGenius on September 4th, 2026
Created on June 12th, 2026
Open Issues & Pull Requests: 20 (+0)
GitHub issues: Enabled
Number of forks: 284
Total Stargazers: 3,938 (+10)
Total Subscribers: 21 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 0.9 hours
Mean response time: 10.5 hours
90th percentile: 26.5 hours
Tracked items: 25

How this project is maintained

Around half of the issues opened in the past year never receive a reply. Only 15% of issues opened in the past year have been closed. Three people close 94% of everything that gets resolved.

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Issue Activity (beta)

Open issues: 14
New in 7 days: 13
Closed in 7 days: 10
Avg open age: 13 days
Stale 30+ days: 0
Stale 90+ days: 0

Recent activity

Opened in 7 days: 13
Closed in 7 days: 10
Comments in 7 days: 9
Events in 7 days: 14

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Detailed Description

Magnitude is an open source inference server that runs local language models optimized for your hardware and integrates with code agents you already use.

The tool solves the problem of running capable language models locally without cloud API costs or privacy concerns. It works by profiling your machine's hardware capabilities, recommending models that fit those constraints, then downloading and tuning them for optimal performance on your specific setup. The server handles model loading on demand, unloading when idle or when memory fills, and applies optimizations like speculative decoding and concurrency tuning automatically.

Magnitude suits developers who want to run agents locally with full privacy and no token costs, particularly those with resource-constrained hardware like Raspberry Pi or machines with limited memory and bandwidth. The tool is designed to work with popular code agents including Pi, OpenCode, Hermes, OpenClaw, Codex, Claude Code, Oh My Pi, and Cline, or you can use its built-in harness. It supports macOS and Linux natively, with Windows support through WSL. The README emphasizes that setup is agent-first: you send a prompt to your agent and it walks through model selection and configuration automatically, rather than requiring manual setup.

The project maintains active engagement with its community through documented channels and shows ongoing development focused on the core inference server and hardware profiling capabilities.