LLM Quantization, Kernels, and Deployment: How to Fine-Tune Correctly, Part 5

The Unsloth deep dive into GPTQ, AWQ, GGUF, inference kernels, and deployment routing Generated using notebookLM A 1.5B model quantized to 4-bit can lose enough fidelity that instruction-following collapses entirely. A GPTQ model calibrated on WikiText and deployed on domain-specific medical text silently degrades on exactly the inputs that matter most. A Mixture-of-Experts model budgeted for 5B active parameters actually needs VRAM for all 400B. None of these failures produce error messages. All of them produce models that look fine on benchmarks and fail in production. The common thread is that the post-training pipeline, everything between the last training step and the first served request, was treated as a formatting step rather than an engineering problem. This episode opens that pip