Can the Apple M2 (8 GB) run Kandinsky 3?

51 pts

Yes, it runs well

Runs well. At FP16 it needs about 4.2 GB of 5.4 GB usable, with an estimated speed of 3.9 min.

Best precision: FP16
Memory mode: Sequential CPU offload
Time per image: 3.9 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 5 days ago against Hugging Face, Ollama and GitHub.

How the C grade is scored

Every result gets up to 100 points from four parts. The points decide the letter grade.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.	21.6 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.	12.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	3.1 / 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
Total	51.7 / 100	51 ptsUsable (C) · 51 of 100 points: Model quality 21.6/45, Memory fit 12.0/25, Speed 3.1/15, Memory use 15.0/15

Grade scale: S 85+ · A 72+ · B 58+ · C 42+ · D 25+ · F under 25 points.

Every precision on the Apple M2 (8 GB)

Precision	Memory needed	Available	Time per output	Memory mode	Fit	Grade · points	Download
`FP16`	4.2 GB	5.4 GB	3.9 min	Sequential CPU offload	Runs well	51 ptsUsable (C) · 51 of 100 points: Model quality 21.6/45, Memory fit 12.0/25, Speed 3.1/15, Memory use 15.0/15	Original weights (opens in a new tab)

Other hardware that runs it

76 ptsGreat (A) · 76 of 100 points: Model quality 21.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 5090 FP16 · 7.5 s
67 ptsGood (B) · 67 of 100 points: Model quality 21.6/45, Memory fit 20.0/25, Speed 11.2/15, Memory use 15.0/15 NVIDIA GeForce RTX 4070 SUPER FP16 · 18 s
67 ptsGood (B) · 67 of 100 points: Model quality 21.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.2/15 NVIDIA RTX PRO 6000 Blackwell FP16 · 6.6 s
67 ptsGood (B) · 67 of 100 points: Model quality 21.6/45, Memory fit 20.0/25, Speed 13.4/15, Memory use 12.5/15 AMD Radeon RX 9070 XT FP16 · 13 s
67 ptsGood (B) · 67 of 100 points: Model quality 21.6/45, Memory fit 20.0/25, Speed 13.4/15, Memory use 12.5/15 NVIDIA GeForce RTX 5080 FP16 · 13 s
67 ptsGood (B) · 67 of 100 points: Model quality 21.6/45, Memory fit 20.0/25, Speed 13.0/15, Memory use 12.5/15 NVIDIA GeForce RTX 4080 SUPER FP16 · 13 s

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.