Hardware capacity is no longer the primary bottleneck for running capable AI locally. Recent devices are running billions of parameters natively, no cloud server involved — a shift that’s happened quietly, outside the usual model-release news cycle, but one that changes what counts as a reasonable default architecture.

For most products built on an LLM API, “cloud-only” was never really a design decision — it was the only option when the project started. Local inference wasn’t fast enough, or the hardware wasn’t common enough to rely on, so every request went out over the network regardless of whether it needed to. That constraint is loosening fast.

Offramp is built for exactly this shift. It taps into the client-side runtime that’s already sitting on a user’s device — the browser’s own on-device model, or a lightweight local runtime — and resolves the structural share of your prompts there: JSON conversion, HTML cleanup, formatting, extraction. Nothing that doesn’t need real reasoning makes the round trip.

The result isn’t “replace your cloud API.” It’s “stop paying cloud latency and cloud token costs on the requests that never needed to leave the device in the first place.” Everything that genuinely requires a frontier model’s reasoning still goes to the cloud exactly like before — nothing changes for the harder stuff, and nothing changes for your users, who just see faster responses and never know the difference.

As on-device capability keeps climbing, the share of traffic that can move local only grows. Building the fallback path now, while it’s still optional, is a lot easier than retrofitting it later under cost pressure.