Beyond the LLM: Mastering Local Small Language Model Orchestration with WebGPU and WASM
Table of Contents Introduction Why Small Language Models Matter on the Edge Fundamentals: WebGPU and WebAssembly 3.1 WebGPU Overview 3.2 WebAssembly Overview Orchestrating Multiple Small Models 4.1 Typical Use‑Cases 4.2 Architectural Patterns Building a Practical Pipeline 5.1 Model Selection & Conversion 5.2 Loading Models in the Browser 5.3 Running Inference with WebGPU 5.4 Coordinating Calls with WASM Workers Performance Optimizations 6.1 Quantization & Pruning 6.2 Memory Management 6.3 Batching & Pipelining Security, Privacy, and Deployment Considerations Real‑World Example: A Multi‑Agent Chatbot Suite Best Practices & Common Pitfalls 10 Future Outlook 11 Conclusion 12 Resources Introduction Large language models (LLMs) have dominated headlines for the past few years, but their sheer size and compute requirements often make them unsuitable for on‑device or edge deployments. In many applications—ranging from personal assistants on smartphones to privacy‑preserving tools on browsers—small language models (SLMs) provide a sweet spot: they are lightweight enough to run locally, yet still capable of delivering useful language understanding and generation. ...