A detailed technical breakdown of three LLM quantization formats for local inference: Q4_K_M (GGUF), AWQ, and FP16. Covers the math behind memory requirements, how each format works internally (k-quants, activation-aware scaling), quality retention benchmarks, speed comparisons, and hardware decision trees. Includes Node.js code examples for loading GGUF models via node-llama-cpp and querying AWQ models through vLLM, plus a React comparison dashboard. Key guidance: Q4_K_M is the most versatile (CPU/GPU/Apple Silicon), AWQ is best for NVIDIA GPU throughput, and FP16 is only practical with 24GB+ VRAM.
Table of contents
Q4_K_M vs AWQ vs FP16 ComparisonTable of ContentsWhat Is Model Quantization and Why Does It Matter for Local LLMs?Understanding Precision Formats: FP16, INT8, INT4Q4_K_M Explained: GGUF and the llama.cpp EcosystemAWQ Explained: Activation-Aware Weight QuantizationFP16: The Uncompressed BaselineHead-to-Head Comparison: Q4_K_M vs AWQ vs FP16Choosing the Right Format for Your HardwarePractical Implementation: Loading Each Format in a Node.js StackImplementation ChecklistCommon Pitfalls and TroubleshootingKey TakeawaysSort: