hjLabs AI Playground
Starting the on-device runtime…
WebAssembly runs ONNX models on the CPU and is supported everywhere; WebGPU hands the same graph to your graphics card and is typically 5–20× faster on transformer workloads, with the gap widening as the model grows. WASM still wins on tiny models where kernel launch overhead dominates, on browsers with no WebGPU, and when you need multi-threading via SharedArrayBuffer. This page lays out the trade-offs — precision support, buffer size limits, load time, and the fp16 trap — and explains how this playground chooses a backend automatically.
webgpu vs wasmwebgpu inference speedonnx runtime web backendbrowser ml performancewasm simd threads aiwebgpu browser supportclient side inference benchmark
Chrome and Edge 113+ on Windows, macOS, ChromeOS and Linux; Safari 26+; Firefox on Windows with wider platform support rolling out. Where it is missing, the WASM backend still works.
WebGPU exposes half precision through the optional "shader-f16" feature, and plenty of otherwise capable adapters — including NVIDIA GPUs under Linux Chrome — report it as unavailable. Always check adapter.features.has("shader-f16") before selecting an fp16 or q4f16 model.