HN Unveils Magnitude (YC S25), a Self‑Optimizing Inference Engine for Agents
agents inference open-source
| Source: HN | Original article
Magnitude, a self‑optimizing inference engine for agents, debuts on Hacker News as part of Y Combinator’s S25 batch.
Magnitude, a Y Combinator S25 startup founded in 2025 by Tom Greenwald and Anders Lie, announced an open‑source inference engine designed specifically for AI agents. The server profiles a user’s hardware, selects the most suitable open‑model, and compiles tuned kernels that run locally. According to the company’s September 30 documentation, the engine delivers up to twice the speed of llama.cpp, with decode performance 92 % faster on Apple‑Silicon Metal and 19 % faster on CUDA‑enabled GPUs. It supports Apple Silicon, NVIDIA, AMD and CPU‑only setups, and integrates with popular agents such as Pi, OpenCode and Hermes with a single click.
The launch matters because it tackles two growing pain points for the agent ecosystem: latency and data privacy. By moving inference from cloud APIs to the user’s own machine, Magnitude eliminates per‑token billing, rate‑limit constraints and the need to transmit potentially sensitive prompts. Faster, on‑device processing also lowers the barrier for deploying asynchronous agents in edge and desktop environments—a trend highlighted in our earlier coverage of “LLMs are General Asynchronous Agents.” As agents become more capable and ubiquitous, tools that keep them performant and private could shape how developers build and scale multi‑agent applications.
What to watch next is whether the engine gains traction among the expanding roster of open‑source agents and how it stacks up against competing local servers such as Ollama, LM Studio and vLLM. Updates to the compiler pipeline, broader hardware support and possible collaborations with larger model providers could further accelerate adoption. The community’s response in the coming weeks will indicate whether self‑optimising inference becomes a new standard for agent‑centric AI workloads.
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