Learn how WebGPU browser AI enables client-side LLM inference with zero server GPU costs. Includes benchmarks, a hands-on tutorial with Transformers.js, and fallback strategies.

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WebGPU enables running large language models directly in the browser using the client's GPU, eliminating server-side inference costs. The compute shader architecture delivers 3-8× performance improvements over WebGL for ML workloads. Libraries like Transformers.js and ONNX Runtime Web provide production-ready implementations. Quantized models up to ~3B parameters run well on consumer hardware. The tutorial demonstrates building a working browser-based LLM with zero backend in under 30 lines of code, including fallback strategies for browsers without WebGPU support. Client-side inference offers strong privacy guarantees and offline capability, though model download size and VRAM constraints remain practical limitations.

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