Magnitude

Overview

Magnitude is a local inference server that solves the most common friction point in running AI models on your own hardware: figuring out which models your machine can actually run, and configuring them correctly. Instead of manually checking VRAM requirements, quantization formats, and context length tradeoffs, you start a Magnitude server and it profiles your hardware, recommends compatible models, and downloads them automatically. The server exposes an OpenAI-compatible API endpoint, so any tool that speaks that protocol can use it without code changes.

Key Capabilities

Hardware profiling and model matching: Magnitude inspects your GPU (VRAM, compute capability), CPU, and available RAM, then recommends models from its curated registry that will run well on your specific setup. This eliminates the trial-and-error of downloading a 7B model only to discover it needs more VRAM than you have, or running a quantized model at a precision level that wastes your available resources.

Automatic download and quantization: Once you select a model, Magnitude handles downloading weights and applying the appropriate quantization. It supports GGUF, GPTQ, and AWQ formats and selects the best quantization level for your hardware automatically.

OpenAI-compatible API: The inference server exposes a standard OpenAI-format API endpoint. Any coding agent, IDE extension, or tool that can point at a custom OpenAI base URL works with Magnitude out of the box — no custom integration needed.

Zero ongoing costs: Once running, there are no API keys, token charges, or rate limits. All inference happens locally on your hardware, which also means complete data privacy — no code or prompts leave your machine.

Use Cases

Developers working on sensitive codebases who cannot send code to cloud APIs use Magnitude to run capable local models with their existing coding agents. Teams with GPU workstations use it to provide shared local inference for the team without per-seat API costs. Individual developers with gaming GPUs or Apple Silicon Macs use it to experiment with open-source models without managing the complexity of llama.cpp, vLLM, or Ollama configuration directly.

Considerations

Local inference quality depends on your hardware — a laptop with 8GB RAM will run smaller models than a workstation with a 24GB GPU. Magnitude makes the best of what you have, but local models are generally less capable than frontier cloud models for complex reasoning and long-context tasks. The server requires initial setup and model downloads, which can be large (several GB per model). Apache 2.0 licensed with an active development community.

Who It’s For

Privacy-conscious developers, air-gapped environments, teams looking to reduce API costs, and anyone who wants to run local models without becoming an MLOps expert. Particularly valuable for developers who already use coding agents and want to add a local inference option alongside their cloud API keys.