How to Deploy and Audit Local LLMs Using the New WebGPU 2.0 Standard
Table of Contents Introduction Why Run LLMs Locally? WebGPU 2.0: A Game‑Changer for On‑Device AI 3.1 Key Features of WebGPU 2.0 3.2 How WebGPU Differs from WebGL and WebGPU 1.0 Setting Up the Development Environment 4.1 Browser Support & Polyfills 4.2 Node.js + Headless WebGPU 4.3 Tooling Stack (npm, TypeScript, bundlers) Preparing a Local LLM for WebGPU Execution 5.1 Model Selection (GPT‑2, Llama‑2‑7B‑Chat, etc.) 5.2 Quantization & Format Conversion 5.3 Exporting to ONNX or GGML for WebGPU Deploying the Model in the Browser 6.1 Loading the Model with ONNX Runtime WebGPU 6.2 Running Inference: A Minimal Example 6.3 Performance Tuning (pipeline, async compute, memory management) Deploying the Model in a Node.js Service 7.1 Using @webgpu/types and headless‑gl 7.2 REST API Wrapper Example Auditing Local LLMs: What to Measure and Why 8.1 Performance Audits (latency, throughput, power) 8.2 Security Audits (sandboxing, memory safety, side‑channel leakage) 8.3 Bias & Fairness Audits (prompt testing, token‑level analysis) 8.4 Compliance Audits (GDPR, data residency, model licensing) Practical Auditing Toolkit 9.1 Benchmark Harness (WebGPU‑Bench) 9.2 Security Scanner (wasm‑sast + gpu‑sandbox) 9.3 Bias Test Suite (Prompt‑Forge) Real‑World Use Cases & Lessons Learned Best Practices & Gotchas 12 Conclusion 13 Resources Introduction Large language models (LLMs) have moved from research labs to the desktop, mobile devices, and even browsers. The ability to run an LLM locally—without a remote API—offers privacy, low latency, and independence from cloud cost structures. Yet, the computational demands of modern transformer models have traditionally forced developers to rely on heavyweight GPU servers or specialized inference accelerators. ...