Stable Diffusion 3.5 Large on Intel N150 NAS (12 GB)

Can the Intel N150 NAS (12 GB) run Stable Diffusion 3.5 Large?

Barely: it runs very slowly (tight fit · encoder unloaded after use)

Very slow (tight fit · encoder unloaded after use). At Q4 GGUF it needs about 7.5 GB of 8.4 GB usable and takes about 47.3 min per image.

Best precision: Q4 GGUF
Memory mode: Text encoder offloaded
Time per image: 47.3 min
Output: 1024×1024, 28 steps

Download Stable Diffusion 3.5 Large

Recommended for the Intel N150 NAS (12 GB): q4_gguf. Every other precision has its own download link in the table below.

Quantized files · GGUF for ComfyUI

Smaller GGUF versions for ComfyUI (with the ComfyUI-GGUF node). No sign-in needed; the model’s license still applies.

Download the q4_gguf (Q4_0) file (opens in a new tab)

huggingface.co/city96/stable-diffusion-3.5-large-gguf (opens in a new tab)

Original weights · Hugging Face

Full-precision files from Stability AI, for fine-tuning, vLLM, Transformers, Diffusers or ComfyUI.

huggingface.co/stabilityai/stable-diffusion-3.5-large (opens in a new tab)

Free Hugging Face account needed: accept the license on the model page and the download starts right away.

Official repo · GitHub

Reference code, inference scripts and docs from Stability AI. The weights themselves download from Hugging Face.

github.com/Stability-AI/sd3.5 (opens in a new tab)

git clone https://github.com/Stability-AI/sd3.5

Links checked 2 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 61Good quality (B) · 61 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at Q4 GGUF, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Very slowtight fit · encoder unloaded after use: How well it runs on the Intel N150 NAS (12 GB): speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.	27.6 / 45
Memory fitFull marks when the model fits in the share of the NAS’s memory that models can use (the rest stays with the NAS system and its storage services); fewer when it goes past that limit.	18.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	0.9 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	15.0 / 15
Match score	61.5 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every precision on the Intel N150 NAS (12 GB)

Quant	Memory needed	Available	Speed	Memory mode	Runs	Quality	Download
`FP16`	8.1 GB	8.4 GB	48.8 min	Sequential CPU offload	Very slowtight fit · CPU offload	66Good quality (B) · 66 of 100: how capable the model is at FP16, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)
`FP8`	5.3 GB	8.4 GB	43.9 min	Sequential CPU offload	Very slowCPU offload	64Good quality (B) · 64 of 100: how capable the model is at FP8, whatever it runs on. Match score 56/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)
`Q8 GGUF`	5.5 GB	8.4 GB	56.1 min	Sequential CPU offload	Very slowCPU offload	65Good quality (B) · 65 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q4 GGUF`	7.5 GB	8.4 GB	47.3 min	Text encoder offloaded	Very slowtight fit · encoder unloaded after use	61Good quality (B) · 61 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)

Other hardware that runs it

64Good quality (B) · 64 of 100: how capable the model is at FP8, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4090 FP8 · 9.9 s · Runs great
64Good quality (B) · 64 of 100: how capable the model is at FP8, whatever it runs on. Match score 83/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 7900 XTX FP8 · 11 s · Runs great
64Good quality (B) · 64 of 100: how capable the model is at FP8, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 FP8 · 8.3 s · Runs great
64Good quality (B) · 64 of 100: how capable the model is at FP8, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 3090 FP8 · 19 s · Runs well
61Good quality (B) · 61 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 9070 XT Q4 GGUF · 18 s · Runs well
61Good quality (B) · 61 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5080 Q4 GGUF · 18 s · Runs well

Quick answers

How much memory does Stable Diffusion 3.5 Large need on the Intel N150 NAS 12GB?

At Q4 GGUF it needs about 7.5 GB. The Intel N150 NAS 12GB has 8.4 GB usable.

How long does Stable Diffusion 3.5 Large take on the Intel N150 NAS 12GB?

About 47.3 min per image at Q4 GGUF and default settings, an estimate scaled from a reference timing by this hardware’s compute speed.

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.