Q5_K_M Quantization

Q5_K_M is a mixed-precision GGUF quantization averaging ~5.7 bits per parameter. Attention and feed-forward weights use 6-bit K-quants, the rest use 5-bit, with per-block scales and importance-weighted matrices.

Q5_K_M is the practical sweet spot for users with enough VRAM to spare. A 7B model fits in ~5 GB and a 70B in ~50 GB. Perplexity vs FP16 is typically 0.05–0.15 points worse — visible on benchmarks but hard to feel in chat.

Pick Q5_K_M over Q4_K_M when you have headroom and the model is doing tasks where small errors compound (long-form writing, multi-turn coding). Pick Q4_K_M instead when VRAM is tight or the model is being used for chat where 0.1 PPL is invisible.