When I started running models locally, I thought quantization meant squeezing more into RAM. Turns o

The article advises against defaulting to Q4_K_M for local LLM inference, emphasizing that optimal performance comes from testing quantization levels tailored to specific workflows. It suggests that aggressive quantization like Q3_K_S can significantly cut latency with imperceptible quality loss for many tasks, though context length presents a trade-off.