Building the internet's inference layer.
We are a small, opinionated team that believes the next era of AI infrastructure should be distributed, private, and honest about its economics.
The problem we're solving
Every AI-powered application today treats cloud inference as a utility — pay per token, accept the latency, trust the provider with your data. That worked when models were research curiosities. It doesn't scale to a world where every SaaS product has an AI copilot and every enterprise has data-residency obligations.
The alternative already exists in users' pockets and on their desks. A modern M-series MacBook can run a 3B parameter model at 40 tokens/second in the browser. An iPhone 15 Pro can serve 34 tokens/second via the Neural Engine. This compute is idle. It's paid for. It's fast. It's private by default.
MeshInfer.AI is the routing and coordination layer that makes that compute useful — without requiring developers to think about WebGPU, ONNX quantization, or peer-to-peer networking.
Our values
Every laptop, phone, and tablet is a node waiting to be used. The infrastructure problem isn't building more GPUs — it's routing to the ones that already exist.
Saying "we don't store prompts" is easy. Making it structurally impossible to store them is the only claim that survives an audit. We built the second kind.
Every benchmark on this site is reproducible. Every architectural decision has a documented rationale. If we can't explain it plainly, we reconsider it.
Users are never silent compute donors. Every mesh participation prompt is explicit, every threshold is user-configurable, and the off switch is always one tap away.