Day 4: Optimizing Model Weights — Quantization Basics (GGUF, AWQ) for Efficient Inference
Production AI runs into the memory wall . LLMs are bound by memory bandwidth, because every generated token moves billions of parameters from memory to the compute cores. A standard 8-billion-parameter model in FP16 needs roughly 16 GB of VRAM for the weights alone, before the KV-cache and context.…
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- 2026-11-10 20:35 · DEV Community — AI
Day 4: Optimizing Model Weights — Quantization Basics (GGUF, AWQ) for Efficient Inference