Int4 Quantization in 2026: How Much Quality Do You Actually Lose?

Quantization is the reason local AI works on ordinary hardware. It is also the setting most people accept without understanding, which is fine until output degrades and nobody knows why.

The short version: four-bit quantization cuts memory to roughly a quarter of full precision, costs a small and usually unnoticeable amount of quality on writing tasks, and is the correct default for almost everyone. Below four bits, things fall apart faster than the file-size savings justify.

Here is the longer version, because the details determine whether you pick correctly.

What Quantization Actually Does

A model’s parameters are numbers. In full precision each is stored as a 16-bit floating point value: two bytes per parameter. A 4-billion-parameter model therefore needs roughly 8GB just to hold its weights.

Quantization stores those numbers with less precision. At four bits, each parameter takes half a byte instead of two, cutting memory to about a quarter. That same 4B model now needs roughly 2.4GB, which is the difference between “runs on a work laptop” and “does not.”

The cost is rounding error. Each weight is slightly wrong, and those errors accumulate through the network. Modern quantization schemes, such as the K-quants implemented in llama.cpp, are clever about this: they keep the parameters that matter most at higher precision and compress the rest harder, which is why a Q4_K_M model performs much better than a naive four-bit conversion would.

The Practical Quality Ladder

Rough guidance based on how these behave on text tasks.

Q8_0, eight bits. Effectively indistinguishable from full precision. Roughly half the memory of 16-bit. Rarely worth it unless you have memory to burn.

Q6, six bits. Very close to lossless. A reasonable choice if you have headroom.

Q5_K_M, five bits. Marginally better than four-bit, marginally more memory. Fine choice.

Q4_K_M, four bits. The default. Measurable degradation on careful benchmarks, hard to notice on writing tasks. Roughly a quarter of full-precision memory. This is what most people should use and what Wrivio ships.

Q3, three bits. Degradation becomes visible. Instruction-following weakens, and the model starts dropping constraints from a multi-clause instruction.

Q2 and below. Substantial damage. Constraints get ignored, output occasionally becomes malformed, and the model loses the reliability you wanted it for. Avoid, even when it is the only way to fit a larger model.

That last point deserves emphasis, because it is the most common mistake: people reach for aggressive quantization to squeeze a bigger model onto their hardware. A well-quantized 4B model beats a badly quantized 30B model at following your instruction, and following the instruction is the entire job for rewriting.

Why The Damage Depends On The Task

Quantization does not degrade all capabilities equally, and this explains why guidance varies so much between sources.

Least affected: register and style transformation, reformatting, short summarization, extraction. These are pattern-level operations that survive rounding error well.

Moderately affected: multi-constraint instruction following. Holding six simultaneous constraints degrades before holding two does, which is worth knowing if your rewrite instructions are long.

Most affected: precise factual recall, arithmetic, code correctness, and long chains of reasoning. Anything where a small error propagates into a wrong answer.

Rewriting sits almost entirely in the first category. Every fact is already in the text you paste, no arithmetic is involved, and no reasoning chain needs to hold. This is why four-bit local models are genuinely adequate for writing work while being a poor choice for code generation, and why people arguing about quantization damage are often arguing about different tasks.

Compression Effects Stack

A subtlety that catches people out.

Many small models available today are not natively small. They are compressed derivatives of much larger models, produced through pruning and distillation. That compression already cost some capability, particularly in long-tail knowledge and multi-constraint recall.

Quantizing such a model adds a second round of degradation on top of the first. Q4 on a compressed model is fine; Q2 on a compressed model has been squeezed twice and behaves accordingly.

If a model card names a much larger parent, treat it as already compressed and be conservative with quantization. There is more in NVIDIA Nemotron and the art of compressing a big model.

Choosing In Practice

A procedure that takes five minutes.

1. Work out your real free memory, not total RAM. Open your usual applications, check usage, subtract, leave 2GB of headroom.
2. Pick the largest model that fits at Q4_K_M within that budget.
3. If it fits with room to spare, try Q5_K_M of the same model rather than jumping to a larger model at lower precision.
4. Test on five real messages before committing.

Step three is the one people get backwards. Given a choice between a bigger model at Q3 and a smaller model at Q5, the smaller model at higher precision is usually the better experience for writing.

There is a fuller selection process in how to choose an open-weights model for your laptop.

How To Tell Quantization Is Too Aggressive

The symptoms are distinctive once you know them.

Constraints get dropped. You asked for no contractions and got contractions. You asked for length parity and got twice the length. A single dropped constraint is a prompt problem; several dropped at once suggests the model has lost precision.

Occasional malformed output. Repeated words, sentences that trail off, odd punctuation, or characters that do not belong. This is the clearest sign.

Facts drift more often. Numbers changing, names slightly altered. Quantization damage shows up here before it shows up in prose quality, which is exactly the failure you least want.

That last symptom is why a word-level diff belongs in a rewriting tool. Fluent text with a changed figure survives a casual reread precisely because it is fluent. Wrivio shows the diff between your original and the rewrite so drift is a glance rather than a careful comparison. See how to review AI rewritten text.

Common Questions

Is int4 the same as Q4_K_M?

Roughly the same precision, different implementations. Int4 is the general term; Q4_K_M is a specific llama.cpp scheme that keeps important weights at higher precision. The K-quant variants perform noticeably better than plain four-bit conversion.

Does quantization make the model faster?

Usually yes, and often substantially, because less memory movement means less time waiting on memory bandwidth. Speed is frequently the bigger practical benefit over the memory saving.

Should I use a bigger model at lower precision or a smaller model at higher precision?

For writing, the smaller model at higher precision, in most cases. Instruction-following reliability matters more than raw capability on constrained tasks.

Where does the GGUF format come in?

GGUF is the container that holds quantized weights plus metadata for llama.cpp-based runtimes. The quantization scheme is part of the file. See GGUF explained for non-engineers.

Download Wrivio for Windows to run a Q4_K_M open-weights model with the quantization choice already made sensibly.