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This presentation outlines the technical feasibility of executing complex AI workloads directly within web browsers using WebGPU and Transformers.js, effectively bringing inference capabilities to the edge without relying on cloud APIs. By leveraging local JavaScript environments, this approach addresses critical data privacy concerns while enabling near-native performance for real-time applications like document generation and interactive dashboards.
Presentation: Running AI at the Edge: Running Real Workloads Directly in the Browser

James Hall discusses the strategic and technical imperative of moving AI workloads from cloud providers to local edge devices. He shares practical approaches using WebGPU, Transformers.js, and DuckDB to achieve near-native performance in JavaScript. Through real-world case studies, he explains how to minimize data privacy risks, optimize browser inference, and build rigorous evaluation suites. By James Hall
James Hall: I'm going to give you a little bit of an intro to myself, some background, just to give you some context of the kinds of things and stuff that I've been working on. I'm going to talk about, why bother? What's the point of running stuff locally? I'm going to intersperse all this with a bunch of demos, so you can actually see and feel how some of this is looking at the moment. I'm going to try and explain at a high level how some of this stuff works, and hopefully give you the pointers that you need to know when you should be using it. I'm also going to give you plenty of pitfalls and takeaways. We've got lots of battle scars. We've made lots of mistakes, so hopefully you can learn from mine. Just a little bit of background. I started a tech consultancy 15 years ago. We basically build digital products for our customers. We've been given all sorts of weird and wonderful briefs, from street lighting to remote unlocking cars to AI dashboards. There's a big wealth of stuff in there. My background, I've been doing weird and wonderful things with JavaScript for…
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