Stop Wrestling with ONNX-to-WebGPU. Start Shipping.
You’ve trained the model. It runs beautifully on your GPU. Then you try to put it in the browser.
Suddenly you’re three tabs deep in ONNX Runtime Web docs, guessing at optimization_level flags, fighting dynamic quantization errors that only appear on Safari, and wondering why your 50 MB model balloons to 200 MB after conversion. The docs are scattered, the GitHub issues are stale, and your launch date doesn’t care.
Browser AI Deployment Toolkit: Convert, Optimize, Ship exists so you never have to guess again.
What’s Inside
- Universal Export Checklists – Battle-tested steps for PyTorch, TensorFlow, and ONNX exports, including the exact opset versions and operator support matrices that actually work in WebGPU today.
- Conversion Pipeline Scripts – Ready-to-run Python and Node CLIs that handle ONNX simplification, constant folding, and WebGPU-friendly graph rewrites in one command.
- Optimization Playbook – Quantization (INT8/INT4), weight palettization, and memory-planning strategies tailored for browser memory limits and WebGPU buffer constraints.
- Browser-Specific Quirk Registry – A living document of Safari/WebKit, Chrome, and Firefox gotchas—texture layout limits, shader compilation timeouts, COOP/COEP headers—with copy-paste workarounds.
Who This Is For
Front-end engineers adding on-device inference to React, Svelte, or vanilla apps. ML engineers tired of handing off “it works on my machine” models. Anyone who wants WebGPU acceleration without spending two weeks reading WGSL specs.
15-Minute Preview
- Clone the repo and run
python convert.py --model your_model.onnx --target webgpu. - Open
dist/index.html—the included demo loads, compiles, and runs inference in < 200 ms on an M2 MacBook. - Swap the model file, re-run, diff the bundle size. That’s your new baseline.
Ship It Today
No more Friday-night debugging sessions. Grab the toolkit for €16.99 and get back to building features.
👉 Buy Browser AI Deployment Toolkit
AI-assisted content - reviewed by author.
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