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Magnitude
Magnitude is an open-source inference server that profiles your computer, recommends local model configurations that fit, downloads the one you choose, and connects it to an agent you already use.
It exposes OpenAI- and Anthropic-compatible APIs on your computer, loads models when an agent requests them, and works with Codex, Claude Code, OpenCode, Pi, Cline, and several other harnesses. An optional Magnitude Harness is included. Use this as a first read, not a recommendation. Open the original project before trusting details like terms, limits, privacy, cost, setup, or safety.
What it is
A hardware-aware local model setup
Magnitude detects the processor, memory, architecture, memory bandwidth, and available acceleration. It ranks model, quantization, and context combinations by expected fit, speed, and capability before you download the full model files.
Why it stands out
Keep the agent, change its model route
Instead of asking you to move into a separate chat app, Magnitude connects its local service to supported coding and agent tools. Models load on demand and unload when idle or when the computer needs memory.
Availability
Active, but still early
The public CLI remains in the 0.0.x series and is changing quickly. Magnitude supports macOS and Linux directly; Windows users must run it through WSL. Setup uses a terminal and downloads the selected model.
Why it matters
What makes it useful
Choosing a local model usually means matching model size, quantization, context length, memory, and acceleration by hand. Magnitude turns those trade-offs into a hardware profile and a ranked list, then keeps the chosen model behind familiar local API shapes for the agent using it.
What to know
Where it fits
Use it when you want an existing coding or agent tool to run against local models and you are comfortable managing a terminal setup, large model downloads, a background service, and early-version changes. It is especially relevant when hardware fit and local inference matter more than using the largest hosted model.
Notable points
What stands out
The local service listens only on the computer's loopback interface, using port 10100. Setup can register it to start when you log in and writes connection settings for the selected harness. For Codex and Claude Code, that local gateway remains part of the connection until you remove it and restart the harness.
Before using
What to review
Read the onboarding steps before giving them to an agent. Setup can install or update Magnitude, profile the machine, download a model, start a background service, and change the selected harness configuration.
Check free disk space before downloading. GGUF model files can be large, and a downloaded model stays on disk even when Magnitude unloads it from memory.
On Windows, install and open WSL first. Native Windows is not the supported runtime.
The documented inference APIs listen only on the computer's loopback interface, using port 10100. Harness setup configures the correct local interface automatically.
Protect the local Magnitude folders. They can contain downloaded models, settings, harness connections, conversations, logs, and traces. Remove prompts, project contents, paths, and credentials before sharing diagnostic logs.
Offline use starts after Magnitude and a model are downloaded. The optional Magnitude Harness web search uses an Exa API key and still calls an outside service.
Expect changes while the CLI is in the 0.0.x series. Check the current release notes before updating and keep a copy of any connection settings you would need to restore.
Reader fit
Who may find it relevant
Developers who want Codex, Claude Code, OpenCode, Cline, or another supported harness to use a local model.
People who want hardware-aware guidance on model size, quantization, context, and likely speed before downloading large files.
Teams testing a loopback OpenAI- or Anthropic-compatible inference endpoint on their own machine.
Less relevant if you need a polished non-technical desktop app, native Windows support, a stable 1.0 release, or hosted-model capability without local setup and storage.
Editorial note
Why LifeHubber lists it
Magnitude makes local model setup easier to inspect: the hardware profile, model files, background service, local gateway, agent connection, and logs all have visible places. LifeHubber lists it because that helps a reader judge whether local control is worth the storage, setup, and early-version maintenance it currently requires.
Source links
Source materials
Reader note
Before relying on this entry
LifeHubber lists entries to help readers inspect AI projects, not to endorse them or prove they are safe, suitable, accurate, maintained, or right for a specific use. We do not verify every entry in depth. Before relying on anything listed, review the original materials, terms, privacy practices, limits, and risks that matter for your situation.
What to explore next
Compare the local runtime with the model and privacy boundary.
Magnitude recommends local models and serves the one you choose to an existing agent. These next pages widen the comparison to other local setups and separate the model choice from the runtime around it.
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