Quantized Models- GGUF, NF4, FP8, FP16 (Complete Guide)

 

You must have been keeping up with the image/video generation models and probably noticed the explosion of diffusion based model variants shared by many developers on Github and Hugging Face.

No matter what Stable Diffusion WebUI (ForgeUI, ComfyUI, Automatic1111, Fooocus etc) you are using sometimes, it is getting a bit confusing in selecting which one to choose. Between these model formats, good old GGUF, and various quantization options.

If you have gone through our model installation tutorials, we share multiple model variants(Ex- Flux, HunyuanVideo, Wan2.1, Stable Diffusion3.5 etc) that can be most suitable for your hardware requirements and use cases. 

Now, we have researched and sharing the experiences to help you clear things up.

Table of Contents:

Comparison Table- GGUF, FP8, FP16, NF4

Feature	FP16	FP8	GGUF Q8_0	GGUF Q5_K	NF4
Bit Precision	16-bit	8-bit	8-bit	5-bit	4-bit
VRAM Usage	Highest	Medium	Medium-High	Low-Medium	Lowest
Image Quality	Reference (Highest)	Very High (95-98% of FP16)	High (90-95% of FP16)	Good (85-90% of FP16)	Acceptable (75-85% of FP16)
Generation Speed	Fast on high-end GPUs	Fast on newer GPUs	Medium	Medium-Fast	Variable (hardware dependent)
Recommended VRAM (Minimum)	8GB+	6GB+	6GB+	4GB+	3GB+
Best For	Final renders, Quality-critical work	Balance of quality and efficiency	General purpose	Limited VRAM scenarios	Highly constrained hardware
CLIP Encoder Speed	Standard	Optimized (Flux)	Standard	Standard	Standard
Hardware Optimization	RTX 3000/4000 series	RTX 3000/4000 series	Broad compatibility	Broad compatibility	Specialized
File Size	Largest	Medium	Medium	Smaller	Smallest
XL Model Support on 8GB VRAM	No	Limited	Limited	Yes	Yes
Quality Degradation	None	Minimal	Slight	Moderate	Noticeable but usable
Community Adoption	High	Growing	High	High	Medium

Different Diffusion based Model formats

There are multiple formats out there in the open source market. But you can get the best suitable setup for your art generation.

(a) Base model

Base models are released as a raw model from its owner and researchers. They are are simple unfiltered, uncensored and widely accessed for the community to do their own research so that they can yield some relevant model in accordance to their use case. 

For instance- An E-commerce startup wants to build a pipeline where they want to do multiple product photoshoot. So, instead of training a model from scratch they will use these base model and fine tune LoRA, do quantization etc. for their use cases.

(b) FP16 (Full Precision)

You can assume this FP16 variant as the "no compromises" option. It is the full 16-bit floating point precision that serves as our standard for quality and not been touched for any specific use case. This is suitable for professionals who do not want their result to be effected by even minimal.

Actually it is hungry for VRAM. You will typically need 12GB or more to run these comfortably. But if you have got the hardware, many users swear this is still the way to go.

(c) GGUF (GPT-Generated Unified Format)

GGUF started in the LLM world but has become a staple for Stable Diffusion too. What's good thing about GGUF is its flexibility. 

Means you can choose your level of compression (Q4_K, Q5_K, Q8_0, etc.) depending on your hardware constraints that suits most. It's the Swiss Army knife of model formats with broad compatibility across different setups.

Many of the developers like City96, Kijai, Quantstack etc. share their quantized models on their github and Hugging Face repositories. You can find by accessing their public repositories.  

The range generally goes from Q2 to Q8. Q8 will give you more precision with detailed result but also consumes more VRAM whereas Q2 generates with lower quality, faster and the VRAM consumption is comparatively lower.

(d) FP8 (8-bit Floating Point)

Generally you will observe these model with Fux. FP8 format is making waves by cutting precision in half compared to the FP16, but with some clever optimizations that preserve quality surprisingly well. 

If you are running newer NVIDIA GPUs like 4000 series, this might be your sweet spot between quality and efficiency.

(e) NF4 (4-bit Normal Float)

For those of you who trying to squeeze impressive images out of modest hardware, NF4 is the great option. This 4-bit quantization reduces memory requirements to its lowest. 

The images won't win any pixel-peeping contests, but they are totally usable for many purposes if its for general use cases. NF4 is part of the BitsAndBytes (BNB) quantization approach and excels at making large models run on limited hardware.