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What is this?

Onyx is a demo website and Python toolkit for running Google's Gemma 4 models directly in your browser using WebGPU. Everything runs locally on your device.

Features

• Multimodal Chat - text, images, and audio, all processed in-browser
• E2B vs E4B Arena - same prompt, two models, side-by-side speed and quality comparison
• Conversion Toolkit - Python scripts to convert, validate, and benchmark Gemma 4 ONNX models

How it works

The demo site uses Transformers.js with WebGPU acceleration to run Gemma 4 E2B (2.3B params, ~3.2 GB total with all encoders) and E4B (~5 GB) in a Web Worker. Models are quantized to 4-bit (q4f16) ONNX format and cached locally after first download.

Demo Site

Requirements

• Chrome 113+ or Edge 113+ with WebGPU enabled
• 4 GB GPU memory for E2B, 8 GB for E4B

Run locally

cd web
npm install
npm run dev

Open http://localhost:5173.

Pages

• / - Landing page with WebGPU compatibility check
• /playground - Multimodal chat with model selection (E2B / E4B)
• /arena - Side-by-side sequential race comparing E2B vs E4B

Conversion Toolkit

Python scripts for converting Gemma 4 models to browser-ready ONNX format.

Setup

cd toolkit
pip install -r requirements.txt

Convert

python convert.py --model google/gemma-4-E2B-it --output output/e2b --quant q4

Options: --quant fp16, --quant q8, --quant q4

Validate

python validate.py --converted output/e2b/onnx_q4 --quick

Or compare against the original:

python validate.py --original google/gemma-4-E2B-it --converted output/e2b/onnx_q4

Benchmark

python benchmark.py --model google/gemma-4-E2B-it --quant-levels fp16 q8 q4

Tech Stack

Models

Models from onnx-community/gemma-4-E2B-it-ONNX and onnx-community/gemma-4-E4B-it-ONNX.

License

MIT