Run models on the user’s GPU to slash round-trips, boost privacy, and smooth p99.
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WebGPU brings real GPU compute to the browser. Learn how to ship on-device ML for lower latency, better privacy, and resilient UX — with code and a rollout plan.
Web apps used to phone home for every smart decision.
Now the browser has a GPU. Real one. With compute. If your model can fit and your UX hates spinners, WebGPU lets you run inference where your users actually are.
Why “in-browser” ML is having a moment
Two pressures collided:
- Latency: Every network hop is a tax. Remove the hop, remove the tax. Users notice.
- Privacy & resilience: Data can stay on device; the app keeps working even when the network hiccups.
- Economics: Serving GPU time in your cloud is pricey. Offloading light/medium models to client GPUs trims bills.
The unlock is WebGPU — a modern, low-level API that exposes general-purpose compute and advanced GPU features to the web, not just graphics. It’s the spiritual successor to WebGL, with compute shaders, better memory control, and a saner programming model for ML.
