Open-source demo runs Qwen models in browser via WASM and WebGPU
A GitHub repository demonstrates how to load and run large language models directly in a web browser using Llama.cpp compiled to WebAssembly (WASM) and WebGPU. The proof-of-concept uses two Qwen models: the smaller Qwen2.5-0.5B (500 million parameters) for faster but less accurate summaries, and Qwen3.5-0.8B (800 million parameters) for higher quality but slower results. Users drag a PDF file…
Key points
- Qwen2.5-0.5B model runs faster but yields less accurate PDF summaries than Qwen3.5-0.8B
- Demo uses WASM and WebGPU to execute models entirely in the browser without cloud servers
- Requires Docker build and local setup with pre-downloaded GGUF model files from Hugging Face
The project requires downloading pre-compiled model files from Hugging Face, building a Docker image, and running it locally. The demo highlights the potential for client-side AI without cloud dependencies, though performance varies significantly by model size. The author, Anthony Budd, provides console logs for debugging and notes the trade-offs between speed and accuracy.
The story so far
2 episodes →- Open-source demo runs Qwen models in browser via WASM and WebGPUthis story
Llama.wasm + WebGPU = Client-side LLMs
github.com · 5 October 2026Loading the full article…
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