Discussion: WebGPU Performance and Local AI Inference

The evolution of WebGPU is finally making 'Local AI' a reality for the average user. Traditionally, running stable diffusion or large language models required a heavy Python environment or expensive cloud GPUs. However, by leveraging the browser's access to local hardware via WebGPU, we can now achieve high-performance inference without any server-side logic. In my project, WebGPU Privacy Studio, I've seen that moving the compute to the client-side not only slashes latency but also fundamentally solves the privacy issue—since the data never leaves the user's machine. I'm curious, for those of you working with WASM or WebGPU, what are the biggest bottlenecks you've hit when trying to port heavy models to the browser?