Most people default to Q4_K_M in llama.cpp because it's the "safe" choice. But I've found the real win comes from testing your actual workflow. A 70B model in Q3_K_S cuts latency significantly compared to Q4_K_M on the same hardware, with imperceptible quality loss for most tasks. The bottleneck becomes memory bandwidth, not raw VRAM size. Here's what changed my setup: I stopped chasing maximum quality and started measuring latency on real prompts. A 4-bit quantized Mistral answers coding questions as well as the full-precision version, but returns results faster. For summarization or creative writing, Q5 variants matter more. For RAG or classification tasks, I can drop to Q3 without noticing the difference. The catch is context length. Lower quantization plus longer context means RAM pressure. If you're doing 4K+ context windows, you can't always drop to the most aggressive quantization. That's where the tradeoff gets real. Spend an hour profiling your use case with different quantization levels.…