This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint.
The MoT design keeps a visual expert stream (reference + future latents) and an action expert stream (state + action), with multi-modal self-attention across both. Weights are sharded at ~10GB per file.
Parameters6B
Context—
Weights24.1 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads3.8k
Runs On
What it takes to serve Giga-World-Policy-0.5 (6B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 12.0 GB | 14.5 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 6.0 GB | 7.2 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 3.0 GB | 3.6 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.
Model Card
By GigaAI, published under apache-2.0, revision b130a71b01ce.
The MoT design keeps a visual expert stream (reference + future latents) and an action expert stream (state + action), with multi-modal self-attention across both. Weights are sharded at ~10GB per file. This repo contains the transformer only; runtime also needs the Wan2.2 VAE / scheduler from the base Diffusers checkpoint. For usage, training, and inference details, see our open source code page.
Read GigaAI's full model card
GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch
- Paper: GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch
- Project page: https://open-gigaai.github.io/giga-world-policy/
- Repository: https://github.com/open-gigaai/giga-world-policy
Model summary
| Field | Value |
|---|---|
| Class | CasualWorldActionTransformer_MoT |
| Layers | 30 |
| Attention heads | 24 × 128 |
| Visual hidden dim | 3072 |
| Action expert dim | 1024 |
| Action FFN dim | 4096 |
| Latent channels | 48 (in/out) |
| Action channels | 16 (in/out) |
| Embodiments | 2 |
| Text dim (T5) | 4096 |
| Patch size | [1, 2, 2] |
The MoT design keeps a visual expert stream (reference + future latents) and an action expert stream (state + action), with multi-modal self-attention across both.
Files
config.json
diffusion_pytorch_model.safetensors.index.json
diffusion_pytorch_model-00001-of-00003.safetensors
diffusion_pytorch_model-00002-of-00003.safetensors
diffusion_pytorch_model-00003-of-00003.safetensors
Weights are sharded at ~10GB per file. This repo contains the transformer only; runtime also needs the Wan2.2 VAE / scheduler from the base Diffusers checkpoint.
Download
# Hugging Face CLI
huggingface-cli download open-gigaai/Giga-World-Policy-0.5 --local-dir ./Giga-World-Policy-0.5
# or Git LFS
git lfs install
git clone https://huggingface.co/open-gigaai/Giga-World-Policy-0.5
Python:
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="open-gigaai/Giga-World-Policy-0.5",
local_dir="./Giga-World-Policy-0.5",
)
For usage, training, and inference details, see our open source code page.
Citation
@article{gigaworld-policy-0.5,
title={GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch},
author={Team, GigaWorld and Ye, Angen and Ma, Angyuan and Wang, Boyuan and Ni, Chaojun and Ye, Fangzheng and Huang, Guan and Li, Guo and Zhao, Guosheng and Yan, Haodong and others},
journal={arXiv preprint arXiv:2607.13960},
year={2026}
}
Configuration
- Attention heads
- 24
Identity and Version
- Repository
- open-gigaai/Giga-World-Policy-0.5
- Publisher
- GigaAI
- Task
- Robotics
- Modality
- Control
- Library
- diffusers
- Parameters
- 6B parameters
- Languages
- Not stated by the source
- Revision
- b130a71b01ceff08aba8c43f7f6ef5e0d4f4d368
- First published
- 2026-07-14
- Last updated
- 2026-07-30
Files and Weights
7 files, 24.1 GB in total. The weights are 3 files totalling 24.1 GB in safetensors.
Weights3 files · 24.1 GB
Configuration2 files · 171.2 KB
Documentation1 file · 2.4 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| diffusion_pytorch_model-00001-of-00003.safetensors | Weights | 10.0 GB | 680d3eb65672 |
| diffusion_pytorch_model-00002-of-00003.safetensors | Weights | 10.0 GB | 5b4896545e2a |
| diffusion_pytorch_model-00003-of-00003.safetensors | Weights | 4.1 GB | 1761ffea0c64 |
| config.json | Configuration | 637 B | — |
| diffusion_pytorch_model.safetensors.index.json | Configuration | 170.6 KB | — |
| README.md | Documentation | 2.4 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 24.1 GB
Download from GigaAI
Released by GigaAI through its official repository on Hugging Face. Read the license.
Built From
- Described by arXiv:2607.13960
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 24.1 GB |
| 16-bit | 12.0 GB |
| 8-bit | 6.0 GB |
| 4-bit | 3.0 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About Giga-World-Policy-0.5
How much GPU memory does Giga-World-Policy-0.5 need?
About 14.5 GB at 16-bit and 3.6 GB at 4-bit: the weights (6B parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run Giga-World-Policy-0.5 on?
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use Giga-World-Policy-0.5 commercially?
Yes. Giga-World-Policy-0.5 is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
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