OmniGen2 on Apple iPhone 17 Pro / 17 Pro Max

Can the Apple iPhone 17 Pro / 17 Pro Max run OmniGen2?

Barely: it runs very slowly (tight fit · encoder unloaded after use)

Very slow (tight fit · encoder unloaded after use). At FP8 it needs about 6.6 GB of 7.2 GB usable, with an estimated speed of 6.4 min.

Best precision: FP8
Memory mode: Text encoder offloaded
Time per image: 6.4 min
Output: 1024×1024, 50 steps

Download OmniGen2

Original weights · Hugging Face

Full-precision files from BAAI (VectorSpaceLab), for fine-tuning, vLLM, Transformers, Diffusers or ComfyUI.

huggingface.co/OmniGen2/OmniGen2 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from BAAI (VectorSpaceLab). The weights themselves download from Hugging Face.

github.com/VectorSpaceLab/OmniGen2 (opens in a new tab)

git clone https://github.com/VectorSpaceLab/OmniGen2

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

How it’s rated

Quality 64Good quality (B) · 64 of 100: how capable the model is at FP8, whatever it runs on. Match score 64/100 (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 slowtight fit · encoder unloaded after use: How well it runs on the Apple iPhone 17 Pro / 17 Pro Max: 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.	29.1 / 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.	18.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	2.4 / 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	64.5 / 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 iPhone 17 Pro / 17 Pro Max

Quant	Memory needed	Available	Speed	Memory mode	Runs	Quality	Download
`FP16`	5.2 GB	7.2 GB	—	—	Won't runover the app memory limit	66Good quality (B) · 66 of 100: how capable the model is at FP16, whatever it runs on. Match score 8/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)
`FP8`	6.6 GB	7.2 GB	6.4 min	Text encoder offloaded	Very slowtight fit · encoder unloaded after use	64Good quality (B) · 64 of 100: how capable the model is at FP8, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

Other hardware that runs it

64Good quality (B) · 64 of 100: how capable the model is at FP8, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 9070 XT FP8 · 19 s · Runs well
64Good quality (B) · 64 of 100: how capable the model is at FP8, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5080 FP8 · 19 s · Runs well
64Good quality (B) · 64 of 100: how capable the model is at FP8, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4080 SUPER FP8 · 20 s · Runs well
64Good quality (B) · 64 of 100: how capable the model is at FP8, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 Ti SUPER FP8 · 23 s · Runs well
64Good quality (B) · 64 of 100: how capable the model is at FP8, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5070 Ti FP8 · 23 s · Runs well
66Good quality (B) · 66 of 100: how capable the model is at FP16, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 3090 FP16 · 30 s · Runs well

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.