Juggernaut XL v9 on Intel Atom C3538 / C3558 NAS (8 GB)

Can the Intel Atom C3538 / C3558 NAS (8 GB) run Juggernaut XL v9?

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

Very slow (tight fit · encoder unloaded after use). At FP8 it needs about 4.7 GB of 5.2 GB usable and takes about 28.8 min per image.

Best precision: FP8
Memory mode: Text encoder offloaded
Time per image: 28.8 min
Output: 1024×1024, 30 steps

Download Juggernaut XL v9

Original weights · Hugging Face

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

huggingface.co/RunDiffusion/Juggernaut-XL-v9 (opens in a new tab)

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

How it’s rated

Quality 60Good quality (B) · 60 of 100: how capable the model is at FP8, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at FP8, 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 Atom C3538 / C3558 NAS (8 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.	27.3 / 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.	1.1 / 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 Atom C3538 / C3558 NAS (8 GB)

Quant	Memory needed	Available	Speed	Memory mode	Runs	Quality	Download
`FP16`	3.8 GB	5.2 GB	39.7 min	Sequential CPU offload	Very slowCPU offload	62Good quality (B) · 62 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`	4.7 GB	5.2 GB	28.8 min	Text encoder offloaded	Very slowtight fit · encoder unloaded after use	60Good quality (B) · 60 of 100: how capable the model is at FP8, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

Other hardware that runs it

62Good quality (B) · 62 of 100: how capable the model is at FP16, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 SUPER FP16 · 8.5 s · Runs great
62Good quality (B) · 62 of 100: how capable the model is at FP16, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 9070 XT FP16 · 6.0 s · Runs great
62Good quality (B) · 62 of 100: how capable the model is at FP16, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 Ti SUPER FP16 · 7.2 s · Runs great
62Good quality (B) · 62 of 100: how capable the model is at FP16, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4080 SUPER FP16 · 6.3 s · Runs great
62Good quality (B) · 62 of 100: how capable the model is at FP16, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5070 Ti FP16 · 7.2 s · Runs great
62Good quality (B) · 62 of 100: how capable the model is at FP16, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5080 FP16 · 6.0 s · Runs great

Quick answers

How much memory does Juggernaut XL v9 need on the Intel Atom C3538 / C3558 NAS 8GB?

At FP8 it needs about 4.7 GB. The Intel Atom C3538 / C3558 NAS 8GB has 5.2 GB usable.

How long does Juggernaut XL v9 take on the Intel Atom C3538 / C3558 NAS 8GB?

About 28.8 min per image at FP8 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.