Kandinsky 3 on Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS (8 GB)

Can the Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS (8 GB) run Kandinsky 3?

Barely: it runs very slowly (CPU offload)

Very slow (CPU offload). At FP16 it needs about 4.2 GB of 5.2 GB usable and takes about 11.4 min per image.

Best precision: FP16
Memory mode: Sequential CPU offload
Time per image: 11.4 min
Output: 1024×1024, 50 steps

Download Kandinsky 3

Original weights · Hugging Face

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

huggingface.co/kandinsky-community/kandinsky-3 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/ai-forever/Kandinsky-3 (opens in a new tab)

git clone https://github.com/ai-forever/Kandinsky-3

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

How it’s rated

Quality 48Fair quality (C) · 48 of 100: how capable the model is at FP16, whatever it runs on. Match score 50/100 (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 Core Ultra 5 225 / 7 265 / 9 285 (desktop) 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.	21.6 / 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.	12.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	1.8 / 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	50.4 / 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 Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS (8 GB)

Quant	Memory needed	Available	Speed	Memory mode	Runs	Quality	Download
`FP16`	4.2 GB	5.2 GB	11.4 min	Sequential CPU offload	Very slowCPU offload	48Fair quality (C) · 48 of 100: how capable the model is at FP16, whatever it runs on. Match score 50/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

Other hardware that runs it

48Fair quality (C) · 48 of 100: how capable the model is at FP16, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 FP16 · 7.5 s · Runs great
48Fair quality (C) · 48 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. NVIDIA GeForce RTX 4070 SUPER FP16 · 18 s · Runs well (encoder in RAM)
48Fair quality (C) · 48 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. NVIDIA RTX PRO 6000 Blackwell FP16 · 6.6 s · Runs great
48Fair quality (C) · 48 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. AMD Radeon RX 9070 XT FP16 · 13 s · Runs great (encoder in RAM)
48Fair quality (C) · 48 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. NVIDIA GeForce RTX 5080 FP16 · 13 s · Runs great (encoder in RAM)
48Fair quality (C) · 48 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. NVIDIA GeForce RTX 4080 SUPER FP16 · 13 s · Runs great (encoder in RAM)

Quick answers

How much memory does Kandinsky 3 need on the Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS 8GB?

At FP16 it needs about 4.2 GB. The Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS 8GB has 5.2 GB usable.

How long does Kandinsky 3 take on the Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS 8GB?

About 11.4 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.