Magnitude is an open-source local inference engine for coding agents that tunes kernels for the user’s device and supports selected text and vision models.
Why it matters
Y Combinator
API Docs
API Available
overview
Magnitude is a local inference engine for coding agents that enables developers to run supported AI models on their own devices. It tunes kernels for the user’s hardware and supports local text and vision inference. Magnitude is open source and lists integrations with Pi, OpenCode, Hermes, OpenClaw, Codex, Claude Code, Oh My Pi, and Cline.
features
Magnitude combines local model inference with device-specific kernel tuning and features aimed at coding-agent workloads. Its product site claims up to 2× the speed of llama.cpp; this is a vendor-stated comparison, not an independently specified benchmark.
use cases
Magnitude is intended for users who want to run supported models locally, particularly in coding-agent workflows. Its listed integrations and supported platforms identify the stated use cases.
how to use
Magnitude’s source code is available at https://github.com/magnitudedev/magnitude, and its API documentation is at https://docs.magnitude.dev. Select a supported platform, model, and coding-agent integration from the options listed by the project.
pricing
The listed Open Source tier is free and provides open-source local inference. The available pricing data does not specify paid tiers, subscription fees, or separate API token charges; the product is also described as freemium, but no paid plan details are provided.
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Similar Tools
Magnitude emphasizes device-specific kernel tuning and coding-agent workloads. The comparisons below describe the stated trade-offs against other local inference and serving tools.
The foundational C/C++ engine for CPU and consumer GPU inference that supports virtually every quantized open-weight model via GGUF.
You get broader architecture support and zero compilation overhead before running models, but you lose Magnitude's automatic per-device kernel auto-tuning and agent-oriented dynamic memory freeing.
Wraps local inference in a streamlined CLI and background daemon with an integrated, Docker-style model registry and standard REST API.
Setting up and swapping models is noticeably simpler and widely supported across coding extensions, but it relies on precompiled generic kernels and lacks Magnitude's agent-specific prefix cache optimizations.
Engineered around high-throughput PagedAttention and continuous batching designed for serving multiple concurrent requests efficiently.
Provides vastly superior multi-session batching and production serving performance, but it is heavy to configure, targets high-end discrete GPUs, and lacks Magnitude's single-device zero-config desktop flow.
Provides a polished desktop GUI for discovering, configuring, and testing local models alongside an instant OpenAI-compatible local server.
Offers a far more user-friendly interface for inspecting model parameters and prompt templates, but it runs standard precompiled llama.cpp binaries without hardware-specific kernel tuning.
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