Q4_K_M vs Q8_0: Which Quantization Should You Download?

Q: Can you notice the difference between Q4_K_M and Q8_0?

For chat and summarization, most people cannot tell the difference. For coding, math, and long multi-step reasoning, the difference is measurable: Q8_0 is effectively lossless (within ~0.1-0.3% perplexity of full precision), while Q4_K_M shows more degradation on these precise tasks.

First, Decode the Names

Both are GGUF quantization levels produced by the llama.cpp toolchain. The names tell you how the weights are stored:

Q4_K_M — weights compressed to ~4-bit precision using k-quantization, with the medium (M) mix of precision levels. K-quants keep the sensitive attention layers at higher precision and compress the tolerant feed-forward layers harder, which is why a Q4 K quant beats a naive 4-bit round.
Q8_0 — weights stored at ~8-bit precision with the simple, classic per-block scheme (the 0 marks the legacy basic quant). No mixed-precision tricks needed — at 8 bits there's very little quality to save.

The practical difference is simple: Q4_K_M packs ~0.56 bytes per parameter; Q8_0 uses ~1.06 bytes per parameter — almost twice the memory for roughly double the precision.

File Size and Memory: The Real Numbers

The table below shows GGUF weights in bytes per parameter and typical sizes for common model families. These are weight sizes — add 1–2GB for the context window (KV cache) and runtime overhead.

Quant	Bytes/param	7B model	14B model	70B model	Quality
F16 (baseline)	2.00	14.0 GB	28.0 GB	140 GB	Reference
Q8_0	1.06	7.4 GB	14.8 GB	74.2 GB	Effectively lossless
Q6_K	0.81	5.7 GB	11.3 GB	56.7 GB	Very good
Q5_K_M	0.69	4.8 GB	9.7 GB	48.3 GB	Good
Q4_K_M	0.56	3.9 GB	7.8 GB	39.2 GB	Acceptable for most tasks
Q3_K_M	0.44	3.1 GB	6.2 GB	30.8 GB	Noticeable loss
Q2_K	0.31	2.2 GB	4.3 GB	21.7 GB	Significant loss

💡 Context matters: a 7B model at Q4_K_M shows 3.9GB of weights, but in practice needs ~5–6GB with context and overhead. A 7B at Q8_0 needs ~8–9GB total. That's why Q8_0 is comfortable on 16GB machines but tight on 8GB ones.

Quality: How Much Do You Actually Lose?

Here's what the benchmarks say (2026 sources):

Q8_0 — effectively lossless. Perplexity increases roughly 0.1–0.3% versus full precision, which is within measurement noise for most tasks. In blind tests, people generally cannot distinguish Q8_0 from the original model.
Q4_K_M — keeps roughly 95–97% of full-precision quality at ~35% of the memory cost, with 2–3× the throughput. For chat, summarization, and general assistance, the loss is barely noticeable. The degradation shows up on precision-critical tasks.

But "quality" is not one number — it depends on what you're doing. Task sensitivity is the real deciding factor:

Task	Sensitivity to quantization	Recommendation
Coding	Highest — one wrong token breaks the logic	Q5_K_M or higher; Q8_0 if you have the RAM
Math & reasoning	High — small errors cascade through long chains	Q5_K_M or Q6_K for chain-of-thought models
Creative writing	Moderate — nuance and rhythm degrade	Q4_K_M acceptable; Q5+ for serious work
Chat & conversation	Low — language tolerates small variations	Q4_K_M is perfectly adequate
Summarization & extraction	Lowest — broad patterns, not fine precision	Q4_K_M handles it well

The Decision Guide

Use your memory situation, not a fixed rule:

Your RAM/VRAM situation	Pick
Model uses less than ~60% of available memory	Q6_K or Q8_0 — you have headroom, spend it on quality. Q6_K is the better default (marginal gain from Q6→Q8, more room for context)
Model uses ~60–80% of available memory	Q5_K_M — the sweet spot between Q4 and Q6
Model uses ~80–95% of available memory	Q4_K_M — the most tested, recommended quant for constrained hardware
Model barely fits even at Q4	Reconsider the model — a smaller model at Q5/Q6 usually beats a bigger one at Q2/Q3

🎯 The rule that beats all tables: a smaller model at a higher quant beats a larger model at a low quant. A sharp 8B model at Q6_K will outperform a mediocre 14B model at Q3_K — and run faster doing it. When in doubt, download Q4_K_M first, run it, and only step up if you can name the quality problem you're seeing.

How to Compare on Your Own Machine

Measure perplexity or speed: llama.cpp ships llama-bench for speed and perplexity comparisons between quant levels.
Inspect a GGUF file: gguf-parser shows exact file metadata, tensor types, and estimated memory use before you commit.
In a GUI: LM Studio shows a color-coded RAM fit indicator per quant; GGUF Loader shows a live RAM/VRAM dashboard as the model loads — no CLI needed.

Need the full setup walkthrough? Our guide to running GGUF models locally covers install, model picks, and troubleshooting, and Ollama vs llama.cpp helps you choose a runtime. For downloads, Local AI Zone lists quantized GGUF files with direct links.

Frequently Asked Questions (FAQ)

Which is better, Q4_K_M or Q8_0?

Q8_0 is higher quality, but Q4_K_M is the better default for most people. Q4_K_M keeps roughly 95–97% of full-precision quality at about a third of the memory cost — the best quality-per-GB trade-off. Choose Q8_0 when you have the RAM/VRAM to spare and the task is sensitive (coding, math, or long structured reasoning).

How much RAM do I need for Q8_0 vs Q4_K_M on a 7B model?

A 7B model at Q4_K_M is about 3.9GB of weights (roughly 5–6GB total with context and overhead), while Q8_0 is about 7.4GB of weights (roughly 8–9GB total). On a 16GB machine, Q8_0 is usually fine; on 8GB, Q4_K_M is the safer choice.

Can you notice the difference between Q4_K_M and Q8_0?

For chat and summarization, most people cannot. For coding, math, and long multi-step reasoning, the difference is measurable: Q8_0 is effectively lossless (within ~0.1–0.3% perplexity of full precision), while Q4_K_M shows more degradation on these precise tasks.

Is Q4_K_M good enough for coding?

Q4_K_M works for simple completions but shows cracks on complex, multi-file coding tasks where a single wrong token breaks the logic. For coding models, Q5_K_M or higher is the community recommendation when your hardware allows it.

Why does Q8_0 exist if Q4_K_M is the default?

Because quality-per-GB is not the only goal. Q8_0 is effectively lossless, so it is the right choice when quality is critical and memory is available — production serving, benchmarking, or tasks where precision errors cascade. Q4_K_M exists to fit more models on modest hardware.

How do I know which quantization to download?

Start with Q4_K_M: it runs almost everywhere and is the default on Ollama and most model hubs. If your RAM/VRAM usage is under ~60%, step up to Q5_K_M or Q6_K. Only use Q8_0 when you have clear headroom and a precision-sensitive task. A smaller model at a higher quant usually beats a larger model at a low quant.