# magnitudedev/magnitude

Source: [GitHub](https://github.com/magnitudedev/magnitude)  
Feed7 permalink: https://feed7.dev/p/magnitude-04ndo8r  
Published: Unknown  
Trust: Needs Review (needs_review)

## Why Included

Magnitude profiles local hardware, recommends compatible models, and configures existing coding-agent harnesses to run them privately and offline.

## Source Summary

Magnitude is an **Apache 2.0** inference server that profiles a machine, recommends fitting local models with estimated speed, then downloads and tunes the selected model. It supports Codex, Claude Code, Cline, OpenCode, Pi, and other harnesses.

## Practical Implication

Use it when local privacy, offline operation, or avoiding API keys and token charges matters. Its CLI can write harness configuration, load models on demand, unload them under memory pressure, and accept compatible GGUF models outside its catalog.

## Agent-Ready Context

Magnitude is an **Apache 2.0** inference server that profiles a machine, recommends fitting local models with estimated speed, then downloads and tunes the selected model. It supports Codex, Claude Code, Cline, OpenCode, Pi, and other harnesses.

Use it when local privacy, offline operation, or avoiding API keys and token charges matters. Its CLI can write harness configuration, load models on demand, unload them under memory pressure, and accept compatible GGUF models outside its catalog.

Support is limited to **macOS and Linux**, with Windows available through **WSL**. There is no fixed hardware minimum, so practical model size and throughput still depend on the machine.

## Connected Context

Feed7 judgment across 691 accumulated Signals:

Magnitude turns local-model use from a manual serving project into a machine-aware setup and lifecycle workflow: it profiles available hardware, recommends models that fit, estimates speed, configures existing coding harnesses, and manages memory pressure. This strengthens the practical case for private, offline agent inference, while narrowing it to macOS, Linux, and WSL and leaving real workload quality and throughput to local testing.

- [unslothai/unsloth](https://feed7.dev/p/unsloth-1l373r0) — Both expose local models to existing coding agents, but Magnitude focuses on automatic hardware profiling, selection, and inference lifecycle management, whereas Unsloth also covers training and export with greater operational complexity.
- [Alishahryar1/free-claude-code](https://feed7.dev/p/free-claude-code-02z06gq) — Both preserve familiar coding harnesses while changing the model backend; Magnitude additionally selects and operates local models, avoiding the credential custody required by a proxy spanning cloud providers.
- [Introducing Cursor Router](https://feed7.dev/p/router-0enx7s0) — Both address model selection, but at different stages: Magnitude recommends locally fitting models from machine capacity, while Cursor Router chooses among models per coding request and cost.

## Context Map

- Layer: tools
- Domains: coding
- Topics: open-models, model-selection, coding-agents

## Uncertainty

- Support is limited to **macOS and Linux**, with Windows available through **WSL**. There is no fixed hardware minimum, so practical model size and throughput still depend on the machine.

## Agent Instruction

Use this item as source-backed context. Do not invent claims beyond the linked source. If this item conflicts with another source, call out the conflict.