hjLabs AI Playground
Starting the on-device runtime…
Quantization is the single biggest lever on whether a model is usable in a browser. fp32 is the reference: largest download, always supported. q8 typically cuts the download by four with quality differences most users cannot detect on encoder models. q4 halves it again and is how a 1.7B chat model fits in a browser at all, at a visible cost in coherence. fp16 and q4f16 look attractive on paper and fail outright on adapters that do not expose the WebGPU "shader-f16" feature — which includes plenty of current desktop GPUs. This page explains what to choose per task and how the playground falls back safely.
onnx quantization comparisonq4 vs q8 qualityfp16 webgpu shader-f16model quantization browserint8 quantization onnxq4f16 not supportedreduce model size inference
Because your WebGPU adapter does not advertise the "shader-f16" feature, so half-precision shaders cannot be compiled. Use q4, q8 or fp32 instead — this site checks the feature and hides options your hardware cannot run.
Both, but the download saving is the reliable part. On WebGPU the speed-up from int8 is real but smaller than on CPU, because the dequantization work partly offsets the reduced memory traffic.