Stable Diffusion 1.5 on Intel Atom C3538 / C3558 NAS (4 GB)

Can the Intel Atom C3538 / C3558 NAS (4 GB) run Stable Diffusion 1.5?

Barely: it runs very slowly (CPU offload)

Very slow (CPU offload). At FP16 it needs about 1.3 GB of 2.0 GB usable and takes about 10.6 min per image.

Best precision: FP16
Memory mode: Sequential CPU offload
Time per image: 10.6 min
Output: 512×512, 25 steps

Download Stable Diffusion 1.5

Original weights · Hugging Face

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

huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/CompVis/stable-diffusion (opens in a new tab)

The original Stable Diffusion code from CompVis; the v1.5 weights are on Hugging Face.

git clone https://github.com/CompVis/stable-diffusion

Links checked 7 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 35Basic quality (D) · 35 of 100: how capable the model is at FP16, whatever it runs on. Match score 44/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at FP16, 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 slowCPU offload: How well it runs on the Intel Atom C3538 / C3558 NAS (4 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.	15.8 / 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.	12.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	1.9 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	14.6 / 15
Match score	44.2 / 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 Atom C3538 / C3558 NAS (4 GB)

Quant	Memory needed	Available	Speed	Memory mode	Runs	Quality	Download
`FP16`	1.3 GB	2.0 GB	10.6 min	Sequential CPU offload	Very slowCPU offload	35Basic quality (D) · 35 of 100: how capable the model is at FP16, whatever it runs on. Match score 44/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

Other hardware that runs it

35Basic quality (D) · 35 of 100: how capable the model is at FP16, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4060 FP16 · 4.3 s · Runs great
35Basic quality (D) · 35 of 100: how capable the model is at FP16, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking. Intel Arc B580 FP16 · 5.0 s · Runs great
35Basic quality (D) · 35 of 100: how capable the model is at FP16, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 3060 12GB FP16 · 4.9 s · Runs great
35Basic quality (D) · 35 of 100: how capable the model is at FP16, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 SUPER FP16 · 2.3 s · Runs great
35Basic quality (D) · 35 of 100: how capable the model is at FP16, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 9070 XT FP16 · 1.6 s · Runs great
35Basic quality (D) · 35 of 100: how capable the model is at FP16, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4060 Ti 16GB FP16 · 3.2 s · Runs great

Quick answers

How much memory does Stable Diffusion 1.5 need on the Intel Atom C3538 / C3558 NAS 4GB?

At FP16 it needs about 1.3 GB. The Intel Atom C3538 / C3558 NAS 4GB has 2.0 GB usable.

How long does Stable Diffusion 1.5 take on the Intel Atom C3538 / C3558 NAS 4GB?

About 10.6 min per image at FP16 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.