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Gemma 4 is demonstrated running in-browser via WebGPU at 255 tokens per second, using kernels generated by Fable 5, showcasing efficient on-device inference.
PrintGuard 2.0 is a major rewrite of a few-shot FDM fault detector using a ShuffleNetV2 backbone and prototypical network, now with a single Python engine that runs unmodified on both CPython and Pyodide in the browser via a platform abstraction layer, enabling per-printer sensitivity tuning and fair inference scheduling.
Trained a prompt injection classifier using ml-intern and DeepSeek V4 Flash, achieving 99% F1 with DistilBERT, optimized to ONNX int8 (~65MB) and deployable in the browser via Transformers.js v3.
The article highlights the new WebGPU backend in llama.cpp/ggml, enabling GPU-accelerated local AI model inference in browsers, developed by Reese Levine and team at USCS over the past year and a half.
A developer demonstrates running the Qwen3.6-27B AI model entirely on WebGPU in a browser, though speed is not optimal.