Production Quantization — AWQ, GPTQ, GGUF K-quants, FP8, MXFP4/NVFP4

Quantization format is not a universal choice — it is a function of hardware, serving engine, and workload. GGUF Q4_K_M or Q5_K_M owns CPU and edge, delivered through llama.cpp and Ollama. GPTQ wins inside vLLM when you need multi-LoRA on the same base. AWQ with Marlin-AWQ kernels delivers ~741 tok/s on a 7B class model with the best Pass@1 at INT4 — the 2026 default for datacenter production. FP8 stays the middle ground on Hopper, Ada, and Blackwell — near-lossless and widely supported. NVFP4 and MXFP4 (Blackwell microscaling) are aggressive and require per-block validation. Two traps bite teams: calibration dataset must match deployment domain, and KV cache is separate from weight quantization — the AWQ lesson "my model is 4 GB now" forgets the 10-30 GB KV cache at production batch sizes.