# Offloading

This page describes how to use offloading techniques for inference to reduce GPU memory usage while maintaining acceptable performance.

## Default Behavior

```python
dit_cpu_offload: bool = True
use_fsdp_inference: bool = False
dit_layerwise_offload: bool = True
text_encoder_cpu_offload: bool = True
image_encoder_cpu_offload: bool = True
vae_cpu_offload: bool = True
pin_cpu_memory: bool = True
lazy_module_load: bool = False
```

On unified-memory accelerators such as NVIDIA GB10 and Apple silicon, FastVideo
detects the selected device inside each worker and disables all five host-offload
modes before loading modules. Host and accelerator allocations share one physical
pool there, so offload adds transfers and duplicate residency instead of freeing
memory. CUDA FSDP sharding remains enabled when requested; MPS continues to
disable FSDP. `pin_cpu_memory` is not an offload mode and is left unchanged.

## Behavior Explanation

!!! note
    For CLI usage, replace underscores (`_`) with hyphens (`-`).

### `use_fsdp_inference`

Enables [FSDP](https://docs.pytorch.org/tutorials/intermediate/FSDP_tutorial.html) for inference. The model weights are sharded across multiple GPUs to reduce memory usage per GPU, and weights are broadcast to all GPUs layer by layer during inference.

#### Performance Impact

FSDP inference introduces negligible performance overhead due to weight prefetching. Performance overhead may be visible when GPU interconnect is slow (e.g., multiple consumer-level GPUs connected by slow PCIe without GPU P2P support).

#### Usage Recommendation

We recommend enabling this option when multiple GPUs are available.

### `dit_cpu_offload`

Enables CPU offloading for FSDP inference. When enabled, the model weights are offloaded to CPU memory, and the weight of each layer is moved to GPU memory only when that layer is being computed.

#### Performance Impact

The PyTorch FSDP implementation does not overlap computation and data transfer perfectly for inference, so enabling this option will harm performance.

#### Usage Recommendation

This option only takes effect when FSDP is enabled. For single GPU usage, we recommend using `dit_layerwise_offload` instead.

### `dit_layerwise_offload`

This option is similar to `dit_cpu_offload`, but with two key differences:

1. It overlaps computation and PCIe data transfer
2. It only works for single GPU inference

#### Performance Impact

This option introduces negligible performance overhead.

#### Usage Recommendation

We recommend enabling this option for single GPU usage. This option is not compatible with FSDP.

### `text_encoder_cpu_offload`

When enabled, the text encoder model weights are offloaded to CPU memory, and text encoding is computed on CPU.

#### Performance Impact

This option significantly slows down text encoding computation, but text encoding is usually not the bottleneck.

#### Usage Recommendation

We recommend enabling this option only when OOM happens.

### `image_encoder_cpu_offload` and `vae_cpu_offload`

When enabled, the weights are stored in CPU memory and moved to GPU memory when the corresponding module is being computed. After computation, the weights are moved back to CPU memory.

#### Performance Impact

These options introduce performance overhead due to PCIe data transfer.

#### Usage Recommendation

We recommend enabling these options when OOM happens.

### `lazy_module_load`

Every option above moves weights between host and device. This one changes
whether they are in memory at all.

By default a pipeline loads every component before the first stage runs, so
peak memory is the sum of all of them even though no two are needed at the same
moment. With `lazy_module_load` enabled, each heavy component loads on first use
and is freed once the last stage that needs it has returned, so peak memory
becomes the largest overlapping set instead of the sum. For a text-to-video
pipeline that is roughly `max(text encoder, DiT + VAE)` rather than
`text encoder + DiT + VAE`.

#### Performance Impact

A freed component is read from disk again on the next generation, so a
multi-prompt run pays one reload per component per request. For a large text
encoder that is tens of seconds.

#### Usage Recommendation

Enable this when a model does not fit at load time, which the CPU offload
options above cannot help with because they act after loading. It is
particularly relevant on unified-memory devices, where host and device draw on
the same pool and moving weights to the host frees nothing. Leave it off when
the model already fits.

This option applies to inference only. Training keeps every component resident
and logs a warning if the flag is set.

## General Recommendations

### Single GPU Inference

We recommend enabling `dit_layerwise_offload`. If OOM happens, also enable `image_encoder_cpu_offload` and `vae_cpu_offload`. If OOM still happens, consider enabling `text_encoder_cpu_offload`.

### Multi-GPU Inference

We recommend enabling `use_fsdp_inference` and disabling both `dit_layerwise_offload` and `dit_cpu_offload`. If OOM happens, consider enabling `text_encoder_cpu_offload`, `image_encoder_cpu_offload`, and `vae_cpu_offload`. If OOM still happens, consider enabling `dit_cpu_offload`.

### When the Model Does Not Fit at Load Time

The offload options only help once loading has finished. If the run dies while
components are still being placed, or if the machine has unified memory so
there is no separate host pool to offload into, enable `lazy_module_load`.

## Examples

### Single GPU with Layerwise Offloading

```python
from fastvideo import VideoGenerator

generator = VideoGenerator.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
    num_gpus=1,
    # Recommended for single GPU
    dit_layerwise_offload=True,
    # Enable if OOM happens
    vae_cpu_offload=True,
    image_encoder_cpu_offload=True,
    text_encoder_cpu_offload=True,
    # Speeds up CPU-GPU transfer
    pin_cpu_memory=True,
)

prompt = "A curious raccoon peers through a vibrant field of yellow sunflowers."
video = generator.generate_video(prompt, output_path="output/", save_video=True)
```

### Multi-GPU with FSDP

```python
from fastvideo import VideoGenerator

generator = VideoGenerator.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
    num_gpus=2,
    # Recommended for multi-GPU
    use_fsdp_inference=True,
    dit_layerwise_offload=False,
    dit_cpu_offload=False,
    # Enable if OOM happens
    vae_cpu_offload=True,
    image_encoder_cpu_offload=True,
    text_encoder_cpu_offload=True,
    pin_cpu_memory=True,
)

prompt = "A majestic lion strides across the golden savanna."
video = generator.generate_video(prompt, output_path="output/", save_video=True)
```