What is the difference between GGUF, GPTQ, and AWQ?

They are different quantization formats. GGUF (used by llama.cpp and Ollama) is the most flexible and supports CPU/GPU offload. GPTQ and AWQ are GPU-focused formats often used with vLLM for faster server inference.

Should I ever go below Q4?

Q3 and Q2 exist to squeeze a bigger model onto smaller hardware, but quality drops more sharply. A Q4 of a smaller model usually beats a Q2 of a larger one.

Q4 vs Q8 Quantization: How Much Quality Do You Lose? (2026)

Q: Is Q4 noticeably worse than Q8?

For most chat, coding, and summarization tasks, a good 4-bit quant (Q4_K_M) is very close to Q8 — typically within 1-2% on benchmarks — while using roughly half the VRAM. Q8 only pulls clearly ahead on the most precision-sensitive tasks.

Quantization is the single biggest lever you have over VRAM. Drop from 16-bit to 4-bit and a model that needed a workstation suddenly fits a gaming GPU. The fear is that you're trading away the model's brain to do it. Mostly, you aren't — and here's the real trade-off.

What "Q4" actually means

An LLM is billions of numbers (weights). At full precision each takes 16 bits. Quantization stores them in fewer bits — 8, 5, 4, even 2 — which shrinks the file and the VRAM footprint. The cost is a tiny rounding error per weight. The art is rounding in a way the model barely notices.

VRAM by bit-depth (per billion parameters)

Precision	~GB per 1B params	A 14B model ≈
FP16 (full)	~2.0 GB	~28 GB
Q8	~1.05 GB	~15 GB
Q4 (Q4_K_M)	~0.55 GB	~7.7 GB
Q2	~0.32 GB	~4.5 GB

The headline Q4 ≈ half the VRAM of Q8, for roughly 1–2% quality loss on typical tasks. That's why Q4_K_M is the default most people should start with — it's the best ratio of capability to memory.

So when is Q8 worth it?

When precision genuinely matters and you have the VRAM to spare: tight math, long structured-output chains, or tasks where a rare wrong token cascades. If your model already fits at Q8 with room left, there's little reason not to. But if going to Q8 forces a smaller model or kicks you into RAM offload, Q4 of the bigger model almost always wins.

GGUF vs GPTQ vs AWQ

These are formats, not quality tiers. GGUF (llama.cpp, Ollama, LM Studio) is the most flexible and the only one that gracefully offloads layers to CPU/RAM — ideal for mixed setups. GPTQ and AWQ are GPU-only formats favored by server runtimes like vLLM for raw throughput. For a desktop or laptop, GGUF is the friendliest starting point.

The calculator lets you flip between Q4, Q8, FP16 and more and watch the VRAM number move in real time — the fastest way to feel this trade-off for your own model.

See quantization change your VRAM live

Pick a model, slide between Q2 → Q4 → Q8 → FP16, and watch the weights + KV cache + overhead total update instantly.

Open the Local AI Calculator →

FAQ

Is Q4 noticeably worse than Q8?

For chat, coding, and summarization a good Q4_K_M is typically within 1–2% of Q8 while using about half the VRAM. The gap only widens on the most precision-sensitive tasks.

What's the best all-round quant?

Q4_K_M (or Q5_K_M if you have a little extra VRAM). It's the sweet spot of quality and memory for most local use.

Does quantization make the model faster?

Often yes — smaller weights mean less memory bandwidth per token, so lower-bit models usually generate text faster, as long as they fit in VRAM.

We may partner with companies or groups to affiliate hardware products based on user needs, earning a commission from qualifying purchases. Memory multipliers are reproducible estimates and vary slightly by quant variant (K_S/K_M/K_L) and runtime. Data current as of June 2026.