Optimizing Local Inference: A Guide to Deploying Quantized 100B Models on Consumer Hardware
Table of Contents Introduction Why 100‑Billion‑Parameter Models Matter Fundamentals of Model Quantization 3.1 Weight vs. Activation Quantization 3.2 Common Bit‑Widths and Their Trade‑offs Consumer‑Grade Hardware Landscape 4.1 CPU‑Centric Systems 4.2 GPU‑Centric Systems 4.3 Emerging Accelerators (TPU, NPU, AI‑Chiplets) Quantization Techniques for 100B Models 5.1 Post‑Training Quantization (PTQ) 5.2 GPTQ & AWQ: Low‑Rank Approximation Methods 5.3 Mixed‑Precision & Per‑Channel Schemes Toolchains and Frameworks 6.1 llama.cpp 6.2 TensorRT‑LLM 6.3 ONNX Runtime + Quantization 6.4 vLLM & DeepSpeed‑Inference Step‑by‑Step Deployment Pipeline 7.1 Acquiring the Model 7.2 Preparing the Environment 7.3 Running PTQ with GPTQ 7.4 Converting to Runtime‑Friendly Formats 7.5 Launching Inference Performance Tuning Strategies 8.1 KV‑Cache Management 8.2 Batch Size & Sequence Length Trade‑offs 8.3 Thread‑Pinning & NUMA Awareness Real‑World Benchmarks Common Pitfalls & Debugging Tips Future Outlook: From 100B to 1T on the Desktop Conclusion Resources Introduction The AI community has witnessed a rapid escalation in the size of large language models (LLMs), with 100‑billion‑parameter (100B) architectures now considered the sweet spot for high‑quality generation, reasoning, and instruction‑following. Historically, running such models required multi‑GPU clusters or specialised cloud instances, making local inference a luxury reserved for research labs. ...