Hitting a CUDA out-of-memory error the moment you load a model is the most common wall people run into when running local AI on a single GPU. Before you start pricing up a bigger card, there is a free fix that lands first almost every time: switch the model to Q4_K_M quantisation. It shrinks the weights enough that a model which refused to load suddenly fits inside the VRAM you already have.

Quick Answer

Q4_K_M cuts a model's memory footprint by roughly 75 percent versus full FP16 precision while keeping about 97 to 99 percent of measured quality. A 7B model that needs around 14GB of VRAM at FP16 drops to roughly 4GB at Q4_K_M, which is what turns an OOM crash into a clean fit on a mid-range card.

What Q4_K_M Actually Does

FP16 stores every weight in a model at 16-bit precision. That is accurate but heavy, which is why a 7-billion-parameter model wants around 14GB just for the weights before you add any context. Quantisation reduces the bits used to store each weight, and Q4_K_M takes them down to roughly 4 bits.

The clever part is the "K_M" tag. This is not a blunt 4-bit crush across the board. K-quant assigns different precision to different layers based on how sensitive each one is, keeping the most important attention tensors at higher precision (Q6_K) while dropping the rest to Q4_K. That mixed approach is why it became the community's default sweet spot: you get most of the memory saving of aggressive quantisation without the quality cliff that uniform 4-bit causes.

How Much Memory You Actually Save

The headline figure is roughly a 75 percent reduction against FP16. In practical terms a 7B model on disk drops from the FP16 size to about 4.1GB at Q4_K_M, around 70 percent smaller, and the in-VRAM footprint falls in step. That difference is exactly what decides whether a model loads on an 8GB or 12GB card or throws an OOM the moment you hit run. Larger models scale the same way, which is how people fit a quantised mid-size model on a single consumer GPU that could never hold the FP16 version.

What You Give Up

For most conversational and instruction-following work the answer is close to nothing. On perplexity benchmarks Q4_K_M holds 97 to 99 percent of FP16 quality, a difference most users never notice in normal use. Coding is the one area where it shows more: on a 7B model you might lose a couple of HumanEval points versus full precision, though on a 70B model the gap shrinks to under a single point. If you write code with a local model and want the tightest accuracy, run a higher quant like Q5 or Q6 if your VRAM allows, and fall back to Q4_K_M when memory is the binding constraint. If you are spec-ing a dedicated local-AI machine, the AI PC range at Evetech focuses on higher-VRAM cards that give you room to run larger quants.

When Quantising Is Not Enough

If you are already on Q4_K_M and still hitting OOM, the model is genuinely too large for the card, or your context window is eating the headroom. Trimming context length, offloading some layers to system RAM, or stepping down to a smaller model are the next moves before any hardware spend. Only once you have exhausted those does a bigger card make sense, and you can compare what is moving fastest on the best-selling PCs at Evetech to see where the value sits.

Frequently Asked Questions

Will Q4_K_M make my model noticeably worse?

For chat, summarising, and general assistant tasks, almost never. It keeps 97 to 99 percent of FP16 quality on perplexity tests. The main exception is heavy code generation, where a small accuracy drop can appear.

How do I switch a model to Q4_K_M?

Download the Q4_K_M GGUF build of the model rather than the FP16 version, or quantise it yourself with llama.cpp's tools. Most popular models are published with a Q4_K_M variant ready to load.

How much VRAM do I need for a 7B model at Q4_K_M?

Roughly 4 to 5GB for the weights, plus extra for context. That is why a 7B model in Q4_K_M comfortably fits cards that cannot hold the 14GB FP16 version.

Is Q4_K_M better than Q4_0?

Generally yes for quality at a similar size. Q4_0 uses uniform 4-bit storage, while Q4_K_M keeps sensitive layers at higher precision, so it holds quality better for the same memory budget.

Running local AI well is mostly about VRAM. Explore the AI-ready systems at Evetech to find a card with enough memory to run larger models and higher quants without the OOM headaches.