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magnitudedev/magnitude

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

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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

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.

Context Map
toolscoding#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.