It is possible to split the large Mochi transformer across multiple GPUs using the `device_map` and `max_memory` options in `from_pretrained`. In the following example we split the model across two GPUs, each with 24GB of VRAM.  
```python
import torch
from diffusers import MochiPipeline, MochiTransformer3DModel
from diffusers.utils import export_to_video

model_id = "genmo/mochi-1-preview"
transformer = MochiTransformer3DModel.from_pretrained(
model_id,
subfolder="transformer",
device_map="auto",
max_memory={0: "24GB", 1: "24GB"}
)

pipe = MochiPipeline.from_pretrained(model_id,  transformer=transformer)
pipe.enable_model_cpu_offload()
pipe.enable_vae_tiling()

with torch.autocast(device_type="cuda", dtype=torch.bfloat16, cache_enabled=False):
frames = pipe(
prompt="Close-up of a chameleon's eye, with its scaly skin changing color. Ultra high resolution 4k.",
negative_prompt="",
height=480,
width=848,
num_frames=85,
num_inference_steps=50,
guidance_scale=4.5,
num_videos_per_prompt=1,
generator=torch.Generator(device="cuda").manual_seed(0),
max_sequence_length=256,
output_type="pil",
).frames[0]

export_to_video(frames, "output.mp4", fps=30)
```
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/mochi.md