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.

An operator running a local writing-assistant workflow on a 24GB RTX 4090 has 20GB of headroom after loading a 32B model's KV cache needs, and is choosing between Q4_K_M and Q5_K_M for a long-form editing task where subtle wording errors compound across multiple drafts. At ~5.7 bits per parameter, Q5_K_M pushes the model to roughly 23GB — still fits with room for a modest context window — versus Q4_K_M's ~19GB. They run both on a multi-turn revision task and the Q5_K_M version holds consistent style and factual details across five rounds of edits where Q4_K_M occasionally drifts. Since VRAM isn't the binding constraint here, they ship Q5_K_M; on a smaller 12GB card running the same 32B model, that headroom wouldn't exist and Q4_K_M would be the only workable choice.