FramePack I2V (HunyuanVideo 13B) on Apple M4 (16 GB)

Can the Apple M4 (16 GB) run FramePack I2V (HunyuanVideo 13B)?

No, it doesn’t fit

No. FramePack I2V (HunyuanVideo 13B) needs about 10.8 GB even at FP8; the Apple M4 (16 GB) has 10.7 GB usable.

Download FramePack I2V (HunyuanVideo 13B)

Original weights · Hugging Face

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

huggingface.co/lllyasviel/FramePackI2V_HY (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/lllyasviel/FramePack (opens in a new tab)

git clone https://github.com/lllyasviel/FramePack

Links checked 2 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 72Great quality (A) · 72 of 100: how capable the model is at FP8, whatever it runs on. Match score 9/100 (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 Won't run: How well it runs on the Apple M4 (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.	9.8 / 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.	0.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	0.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	0.0 / 15
Match score	9.8 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

It doesn’t fit, so only a reduced share of the quality points counts and fit, speed and memory use score zero.

Every precision on the Apple M4 (16 GB)

Quant	Memory needed	Available	Speed	Memory mode	Runs	Quality	Download
`FP16`	15.3 GB	10.7 GB	—	—	Won't run	74Great quality (A) · 74 of 100: how capable the model is at FP16, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)
`FP8`	10.8 GB	10.7 GB	—	—	Won't run	72Great quality (A) · 72 of 100: how capable the model is at FP8, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

Hardware that can run it

72Great quality (A) · 72 of 100: how capable the model is at FP8, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 FP8 · 5.0 min · Runs well (tight fit)
74Great quality (A) · 74 of 100: how capable the model is at FP16, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX PRO 6000 Blackwell FP16 · 4.9 min · Runs well
72Great quality (A) · 72 of 100: how capable the model is at FP8, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4090 FP8 · 6.3 min · Runs well (encoder in RAM)
72Great quality (A) · 72 of 100: how capable the model is at FP8, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 7900 XTX FP8 · 7.0 min · Runs well (encoder in RAM)
72Great quality (A) · 72 of 100: how capable the model is at FP8, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 3090 FP8 · 11.8 min · Runs well (encoder in RAM)
72Great quality (A) · 72 of 100: how capable the model is at FP8, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. AMD Ryzen AI Max+ 395 (128 GB) FP8 · 11.5 min · Runs well

Models that do run on the Apple M4 (16 GB)

49Fair quality (C) · 49 of 100: how capable the model is at FP8, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking. LTX-Video 2B FP8 · 6.9 min
55Good quality (B) · 55 of 100: how capable the model is at FP16, whatever it runs on. Match score 62/100 (quality, fit, speed and memory use) sets the ranking. Wan 2.1 T2V 1.3B FP16 · 73.2 min
74Great quality (A) · 74 of 100: how capable the model is at FP8, whatever it runs on. Match score 62/100 (quality, fit, speed and memory use) sets the ranking. HY-OmniWeaving (8.3B, unified video) FP8 · 3.0 h
53Fair quality (C) · 53 of 100: how capable the model is at FP8, whatever it runs on. Match score 62/100 (quality, fit, speed and memory use) sets the ranking. Wan 2.1 VACE 1.3B FP8 · 71.4 min
35Basic quality (D) · 35 of 100: how capable the model is at FP16, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking. AnimateDiff v3 (SD 1.5) FP16 · 5.8 min

Quick answers

How much memory does FramePack I2V (HunyuanVideo 13B) need on the M4 16GB?

Even at FP8 it needs about 10.8 GB; the M4 16GB has 10.7 GB usable.

What can the M4 16GB run instead?

LTX-Video 2B, Wan 2.1 T2V 1.3B and HY-OmniWeaving (8.3B, unified video) run best on it; the page lists more, each with its speed.

What hardware can run FramePack I2V (HunyuanVideo 13B)?

RTX 5090 32GB, NVIDIA RTX PRO 6000 Blackwell 96GB and RTX 4090 24GB run it; the page lists more, each with its 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.