What Quantization Does to a Local LLM (Q4 vs Q8)

Graphics card installed in a PC motherboard, close-up of cooling fans

Every model download on Hugging Face makes you pick a quantization: Q4_K_M, Q8_0, F16. Those labels control two things: how much disk space the model needs and how much video memory (VRAM) it takes to run. The right pick decides whether a model fits on your graphics card at all.

Quantization shrinks the model's weights. A full-precision 8B model needs about 16 GB just to load. The same model at Q4 fits in about 5.5 GB, and Q8 needs around 9 GB. That is the entire difference between needing a 24 GB card and getting by with a mid-range 12 GB one.

The quality loss is smaller than most people expect. For chat and general questions, Q4 gives you nearly everything the full model offers. The gap shows up in hard reasoning and code generation, where Q8 holds up better. My rule: start with Q4. If the answers wobble on your hardest prompts, step up to Q8 and pay the bigger VRAM bill.

Memory math has one more term: context. A long conversation fills the key-value cache, which eats VRAM on top of the weights. A 32K-context session can add several gigabytes, so leave headroom instead of packing the card to the last byte.

Before you buy a card for a specific model, run the numbers together: the VRAM calculator on llmhardware.io. It sizes weights, quantization level, and context length so you know exactly what fits.

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