Krea 2 Turbo on Intel Celeron J6412 / Atom x6425E NAS (16 GB)

Can the Intel Celeron J6412 / Atom x6425E NAS (16 GB) run Krea 2 Turbo?

Barely: it runs very slowly (CPU offload)

Very slow (CPU offload). At FP8 it needs about 7.1 GB of 12.0 GB usable and takes about 34.1 min per image.

Best precision: FP8
Memory mode: Sequential CPU offload
Time per image: 34.1 min
Output: 1024×1024, 8 steps

Download Krea 2 Turbo

Original weights · Hugging Face

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

huggingface.co/krea/Krea-2-Turbo (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 Krea. The weights themselves download from Hugging Face.

github.com/krea-ai/krea-2 (opens in a new tab)

git clone https://github.com/krea-ai/krea-2

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

How it’s rated

Quality 90Excellent quality (A+) · 90 of 100: how capable the model is at FP8, whatever it runs on. Match score 67/100 (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 slowCPU offload: How well it runs on the Intel Celeron J6412 / Atom x6425E NAS (16 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.	40.8 / 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.	1.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	13.7 / 15
Match score	67.6 / 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 Celeron J6412 / Atom x6425E NAS (16 GB)

Quant	Memory needed	Available	Speed	Memory mode	Runs	Quality	Download
`FP16`	11.6 GB	12.0 GB	37.9 min	Sequential CPU offload	Very slowtight fit · CPU offload	92Excellent quality (A+) · 92 of 100: how capable the model is at FP16, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)
`FP8`	7.1 GB	12.0 GB	34.1 min	Sequential CPU offload	Very slowCPU offload	90Excellent quality (A+) · 90 of 100: how capable the model is at FP8, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

Other hardware that runs it

90Excellent quality (A+) · 90 of 100: how capable the model is at FP8, whatever it runs on. Match score 95/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 7900 XTX FP8 · 4.3 s · Runs great
90Excellent quality (A+) · 90 of 100: how capable the model is at FP8, whatever it runs on. Match score 95/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 3090 FP8 · 7.3 s · Runs great
90Excellent quality (A+) · 90 of 100: how capable the model is at FP8, whatever it runs on. Match score 95/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4090 FP8 · 3.9 s · Runs great
90Excellent quality (A+) · 90 of 100: how capable the model is at FP8, whatever it runs on. Match score 95/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 FP8 · 3.2 s · Runs great
92Excellent quality (A+) · 92 of 100: how capable the model is at FP16, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 Max (64 GB) FP16 · 25 s · Runs well
92Excellent quality (A+) · 92 of 100: how capable the model is at FP16, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking. AMD Ryzen AI Max+ 395 (128 GB) FP16 · 8.3 s · Runs great

Quick answers

How much memory does Krea 2 Turbo need on the Intel Celeron J6412 / Atom x6425E NAS 16GB?

At FP8 it needs about 7.1 GB. The Intel Celeron J6412 / Atom x6425E NAS 16GB has 12.0 GB usable.

How long does Krea 2 Turbo take on the Intel Celeron J6412 / Atom x6425E NAS 16GB?

About 34.1 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.