Modern browsers include a GPU, a neural-network runtime, and enough compute to run useful AI models locally. That creates a different architecture for features that need privacy, offline operation, or low-latency interaction: no inference request has to leave the device.

This session runs text generation, image classification, and semantic search directly in a browser tab using Transformers.js, ONNX Runtime, and WebGPU. Along the way, we will examine the limits that determine whether a model belongs in the browser: download size, quantization, warm-up time, memory pressure, browser support, and the point where a server is still the better answer.

Attendees will leave able to select browser-appropriate models, choose between WebGPU and WASM fallbacks, design an offline-capable inference flow, and explain the privacy and operational tradeoffs of client-side AI.

Audience: Web developers and architects evaluating client-side AI.
Format: 45–60-minute technical talk with live browser demonstrations.
Demo: Local text generation, image classification, and semantic search with no inference backend.
Technology reference: https://github.com/huggingface/transformers.js
Evidence: New reusable session; currently in evaluation for Live! 360 Tech Con Orlando 2026.
Materials: Talk-specific repository, performance notes, slides, and recording are not yet published.
Vendor scope: Vendor-neutral web standards and open-source runtimes.