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. ...

Introduction Large Language Models (LLMs) such as GPT‑4, Claude, and LLaMA have transformed how we generate text, answer questions, and build intelligent assistants. A common pattern in production LLM pipelines is retrieval‑augmented generation (RAG), where the model queries an external knowledge store, retrieves the most relevant pieces of information, and conditions its response on that context. The retrieval component must be fast, scalable, and accurate—especially for real‑time applications like chatbots, code assistants, or recommendation engines where latency directly impacts user experience and business value. Vector databases (e.g., Milvus, Pinecone, Weaviate, Qdrant, FAISS) are the de‑facto storage and search layer for high‑dimensional embeddings. Optimizing these databases for real‑time retrieval is a multi‑dimensional problem that touches hardware, indexing algorithms, data layout, query routing, and observability. ...

Introduction Large language models (LLMs) have moved far beyond conversational chatbots. Modern deployments increasingly place local LLM agents on edge devices—industrial controllers, IoT gateways, autonomous robots, and even smartphones—to run autonomous workflows without reliance on a central cloud. This shift promises lower latency, stronger data privacy, and resilience in environments with intermittent connectivity. Yet, simply loading a model onto an edge node and issuing prompts is rarely enough. Edge workloads have strict constraints on compute, memory, power, and network bandwidth. To unlock the full potential of local LLM agents, developers must think like system architects: they need to optimize model selection, inference pipelines, memory management, and orchestration logic while preserving the model’s reasoning capabilities. ...

Introduction Large language models (LLMs) have moved from research curiosities to everyday tools—code assistants, chatbots, and domain‑specific copilots. While cloud‑based inference remains popular, a growing segment of developers, enterprises, and privacy‑focused organizations prefer local inference: running models on on‑premise hardware or edge devices. The promise is clear—data never leaves the premises, latency can be reduced, and operating costs become more predictable. However, local inference is not without friction. The most common bottleneck is memory: modern transformer models often require hundreds of gigabytes of RAM or VRAM, and the bandwidth needed to move weights and activations quickly exceeds what traditional CPU‑GPU memory hierarchies can deliver. In 2026, the industry is converging on a Universal Memory Architecture (UMA) that unifies volatile, non‑volatile, and high‑bandwidth memory under a single address space, dramatically reshaping how we think about LLM deployment. ...

Table of Contents Introduction Why 100 B‑Parameter Models Matter Hardware Landscape for Local Inference 3.1 GPU‑Centric Setups 3.2 CPU‑Only Strategies 3.3 Hybrid Approaches Fundamental Techniques to Shrink the Memory Footprint 4.1 Precision Reduction (FP16, BF16, INT8) 4.2 Weight Quantization with BitsAndBytes 4.3 Activation Checkpointing & Gradient‑Free Inference Model‑Specific Optimizations 5.1 LLaMA‑2‑70B → 100B‑Scale Tricks 5.2 GPT‑NeoX‑100B Example Efficient Inference Engines 6.1 llama.cpp 6.2 vLLM 6.3 DeepSpeed‑Inference Practical Code Walk‑Through Benchmarking & Profiling Best‑Practice Checklist Future Directions & Emerging Hardware 11 Conclusion 12 Resources Introduction Large language models (LLMs) have exploded in size, with 100‑billion‑parameter (100 B) architectures now delivering state‑of‑the‑art performance on tasks ranging from code generation to scientific reasoning. While cloud providers make these models accessible via APIs, many developers, researchers, and hobbyists prefer local inference for privacy, latency, cost, or simply the joy of running a massive model on their own machine. ...