Browser-Native LLM Inference: The WebGPU Engineering You Didn't Know You Needed
Most AI features are architected the same way: user input travels to an API, a cloud GPU processes it, and a response travels back. That round trip is so normalized that engineers rarely question it. But it carries a hidden tax: 200–800ms of network latency on every interaction, an API key that must live somewhere accessible (and therefore vulnerable), and a hard dependency on uptime you don't control.
Browser-native LLM inference via WebGPU breaks all three of those assumptions. The model runs on the user's GPU, inside a browser sandbox, with no network round-trip. This isn't a future capability — as of late 2025, WebGPU ships by default across Chrome, Firefox, Edge, and Safari, covering roughly 82.7% of global browser traffic. The engineering question has shifted from "can we do this?" to "when does it beat the cloud, and how do we route intelligently between the two?"
