How to Quantize a Local LLM in 2026 (GGUF Q4 vs Q6 vs Q8 — and What You Lose)

Quantization is the trick that lets a 16GB card run models it has no business running. Here's how GGUF quant levels work, which one I default to,… Aliteq Lena Fischer · AI & Local Compute Editor

Quantization = lower-precision weights = less memory. FP16 → 4-bit roughly quarters the memory footprint.

Q4_K_M is my default. ~4–5 bits per weight, about half the size of an 8-bit copy and a quarter of FP16, with quality loss most people can't spot in normal use.

Go higher (Q5_K_M / Q6_K / Q8_0) only if you have spare VRAM and want the last few percent — coding and precise reasoning benefit most.

Q3 and Q2 are last resorts — real, visible degradation; only worth it to squeeze a model onto a card that otherwise can't hold it at all.

The math: memory ≈ (params × bits ÷ 8) + ~1–2GB overhead. A 30B model is ~60GB at FP16 but ~18–20GB at Q4 — the difference between 'needs a datacenter' and 'runs on a used 3090.'

You rarely quantize yourself — you download a ready-made GGUF from Hugging Face, or let Ollama pull one.

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