Kandinsky 6.0 Video Lite (3B, video + audio) on Apple M4 Pro (48 GB)

Can the Apple M4 Pro (48 GB) run Kandinsky 6.0 Video Lite (3B, video + audio)?

Barely: it runs very slowly (tight fit)

Very slow (tight fit). At FP16 it needs about 31.5 GB of 36.0 GB usable and takes about 50.9 min per clip.

Best precision: FP16
Memory mode: Everything on GPU
Time per clip: 50.9 min
Output: 864×480, 121 frames, 50 steps

Best app to run it on the Apple M4 Pro (48 GB)

On macOS

ComfyUI

Desktop app · runs on PyTorch (ComfyUI), GGUF via ComfyUI-GGUF

Runs this model with built-in or popular custom nodes; GGUF files load with ComfyUI-GGUF.

Get ComfyUI

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download Kandinsky 6.0 Video Lite (3B, video + audio)

Original weights · Hugging Face

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

huggingface.co/kandinskylab/Kandinsky-6.0-Lite-5s-Diffusers (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/kandinskylab/kandinsky-6 (opens in a new tab)

git clone https://github.com/kandinskylab/kandinsky-6

Links checked 6 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 68Good quality (B) · 68 of 100: how capable the model is at FP16, whatever it runs on. Match score 72/100 (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 slowtight fit: How well it runs on the Apple M4 Pro (48 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.	30.6 / 45
Memory fitFull marks when the model fits in the share of unified memory the GPU can use; fewer when it goes past that limit.	23.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	3.6 / 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	72.2 / 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 Apple M4 Pro (48 GB)

Quant	Memory needed	Available	Speed	Memory mode	Runs	Quality	Download
`FP16`	31.5 GB	36.0 GB	50.9 min	Everything on GPU	Very slowtight fit	68Good quality (B) · 68 of 100: how capable the model is at FP16, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

Other hardware that runs it

68Good quality (B) · 68 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 RTX PRO 6000 Blackwell FP16 · 3.8 min · Runs great
68Good quality (B) · 68 of 100: how capable the model is at FP16, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4090 FP16 · 5.4 min · Runs well (encoder in RAM)
68Good quality (B) · 68 of 100: how capable the model is at FP16, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 7900 XTX FP16 · 6.0 min · Runs well (encoder in RAM)
68Good quality (B) · 68 of 100: how capable the model is at FP16, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 FP16 · 4.5 min · Runs well (encoder in RAM)
68Good quality (B) · 68 of 100: how capable the model is at FP16, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 9070 XT FP16 · 7.2 min · Runs well (tight fit · encoder in RAM)
68Good quality (B) · 68 of 100: how capable the model is at FP16, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5080 FP16 · 7.2 min · Runs well (tight fit · encoder in RAM)

Quick answers

How much memory does Kandinsky 6.0 Video Lite (3B, video + audio) need on the M4 Pro 48GB?

At FP16 it needs about 31.5 GB. The M4 Pro 48GB has 36.0 GB usable.

How long does Kandinsky 6.0 Video Lite (3B, video + audio) take on the M4 Pro 48GB?

About 50.9 min per clip 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.