WebGPU: Your Browser Just Got Superpowers (And It's About Time)
WebGPU distributes the computational load across users’ devices, making high-fidelity, AI-powered experiences scalable without requiring every pixel to be streamed from the cloud.
Hey there!
Samet here, and this week I want to talk about something that’s quietly revolutionizing what’s possible in your browser, something that could fundamentally change how you think about building AI-powered products.
WebGPU has officially landed across all major browsers in 2025, and it’s not just another incremental update.
This is the kind of shift that turns “impossible in the browser“ into “surprisingly fast in the browser.“
The Performance Leap You’ve Been Waiting For
Let’s cut to the chase:
WebGPU delivers 10x faster performance than WebGL for AI workloads.
We’re talking about sub-30ms inference times for language models and 20-60 tokens per second for real-time AI reasoning—all running locally in your user’s browser.
Chrome, Edge, Firefox, and Safari now all support WebGPU, meaning you can finally tap into genuine GPU acceleration without asking users to install anything.
Chrome and Edge have supported it since version 113 on Windows (Direct3D 12), macOS, and ChromeOS. Firefox shipped WebGPU in version 141 for Windows, and Safari 26 brought native support across macOS, iOS, and iPadOS.
The browser coverage is substantial. When weighted by market share, the vast majority of users already have access to WebGPU-capable browsers. For product teams, this means you can start building GPU-accelerated experiences today without worrying about leaving your users behind.
Why This Matters for Your Product
WebGPU isn’t just faster. It’s fundamentally different. Unlike WebGL, which required awkward workarounds to repurpose graphics APIs for computation, WebGPU treats compute as a first-class capability. This opens up entirely new categories of browser-based applications:
AI that runs offline: Store models in IndexedDB using TensorFlow.js, and run inference completely client-side. Better privacy, zero API costs, and no latency from network calls. Libraries like Transformers.js and ONNX Runtime already leverage WebGPU to enable high-speed model inference directly in browsers.
High-performance 3D experiences: Neural rendering techniques like 3D Gaussian Splatting now run smoothly in browsers, enabling immersive web experiences that previously required native apps. Teams are building real-time rendering and XR applications with performance that rivals desktop applications.
General-purpose computation: Physics simulations, particle systems, image processing—workloads that were fragile or impractical in WebGL become natural with WebGPU’s explicit compute shaders. One healthcare SaaS firm reduced script maintenance by 70% after switching to WebGPU-based workflows.
The Developer Experience Has Changed
Here’s what makes WebGPU particularly exciting from a product development standpoint: it reduces CPU overhead through explicit pipelines and bind groups, making performance more predictable across platforms.
You get explicit memory management for better optimization and native support for compute shaders that allow advanced GPU computations.
The programming model is cleaner and more flexible than WebGL ever was. You’re not fighting the API to do general computation—you’re working with an interface designed for it from the ground up. This means faster development cycles and more maintainable code.
Real-World Applications Already Shipping
Teams aren’t waiting to experiment with WebGPU—they’re shipping production applications:
Medical AI platforms are using browser-based ML inference with WebGPU for clinical decision support, running complex models entirely client-side
Gaming and graphics companies are delivering desktop-class rendering experiences directly in browsers
Data visualization tools are leveraging WebGPU’s compute capabilities for real-time processing of massive datasets
The technology has matured beyond proof-of-concept. With FP16 support, models run 40% faster with minimal accuracy loss. TensorFlow.js reports 3x faster inference with the WebGPU backend compared to WebGL, and performance gains increase with model complexity.
Your Move
The implications are clear: the browser is no longer a second-class platform for compute-intensive work. WebGPU distributes the computational load across users’ devices, making high-fidelity, AI-powered experiences scalable without requiring every pixel to be streamed from the cloud.
For product teams, this means rethinking what’s possible. Do you know if your AI features run client-side instead of on expensive GPU infrastructure? Could your data visualization tools handle larger datasets with GPU acceleration? What experiences were you avoiding because “browsers can’t handle it“?
The constraints you’ve been designing around for years? Many of them just disappeared. The question is: what will you build now that the browser has superpowers?
Start small by trying to run a lightweight ML model with TensorFlow.js on WebGPU. Prototype a GPU-accelerated feature that seemed impossible last year. The computing power is already in your users’ hands. All you need to do is tap into it.
Until next time, keep building the future,
Samet Özkale, AI for Product Power
AI Product & Design Manager |
https://samet.works
P.S. If you’re experimenting with WebGPU in your products, I’d love to hear about it. Reply to this email and share what you’re building—I’m always curious to see how teams are pushing the boundaries of what’s possible in the browser.
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