Instructions to use XXXXyu/Cosmos3-Edge-Policy-DROID-GenW8A8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Cosmos
How to use XXXXyu/Cosmos3-Edge-Policy-DROID-GenW8A8 with Cosmos:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Cosmos3 Edge Policy DROID GenW8A8
Base model | Cosmos Lite | Benchmark | Cosmos 3 report
Run NVIDIA's 4B Cosmos3 Edge DROID robot policy on a single 24GB RTX 4090. This self-contained GenW8A8 deployment is the recommended Cosmos Lite tradeoff for Edge.
At A Glance
| Model | Quant. | VRAM (GB) | Request p50 (ms) | RoboLab-120 SR (%) |
|---|---|---|---|---|
| Cosmos3 Edge 4B | GenW8A8 | 8.79 | 331.4 | 19.25 |
The release protocol is one RTX 4090, batch size one, guidance 3, two UniPC denoise steps, shift 5, and a 32-action chunk. The result is 231/1,200 closed-loop rollouts across 120 RoboLab tasks, 10 episodes per task, using default instructions. Request latency excludes simulator time.
A representative Edge GenW8A8 closed-loop RoboLab timing trace is shown above. It uses the validated guidance 3 / two-step sampler.
Model Overview
Cosmos3 Edge is NVIDIA's 4B Mixture-of-Transformers world action model for DROID. It predicts robot actions from language, RGB observations, and robot state. Cosmos Lite changes the inference representation and runtime; it does not retrain or distill the policy.
GenW8A8 applies dynamic per-token FP8 E4M3 W8A8 to 168 action-generation linear modules. The other 168 selected language/MoT linear modules use packed W4A16; activations outside the W8A8 modules remain BF16. The bundle is self-contained and does not require the BF16 source checkpoint or calibration dataset at deployment time.
Calibration And Evaluation
No additional policy training is performed. W4 scales use input-channel
statistics from 128 successful episodes in the official
nvidia/Cosmos3-DROID training split. Calibration does not update model
parameters and uses no RoboLab evaluation episode. FP8 activation scales are
computed dynamically per token at inference time.
The public result above covers all 120 RoboLab tasks. Full protocol, confidence intervals, topology, and memory definitions are in the Cosmos Lite benchmark.
Policy Interface
The validated RoboLab client composes three camera views into a 640x540 RGB
input and maps it to the model's 736x544 inference bucket. It returns a 32x8
DROID joint-position action chunk; the standard integration executes all 32
actions before requesting another chunk.
Model precision is fixed by this artifact. Guidance, denoise steps, and shift are independent runtime controls. The release config defaults to guidance 3, two denoise steps, and shift 5.
Benchmark Results
The primary release result is the full RoboLab-120 evaluation above. It is compared with Edge BF16 in the main benchmark; sampler and legacy quantization experiments are separated into RoboLab ablations. The recommended role of this artifact is low-latency Edge inference; the official NVIDIA BF16 checkpoint remains the original-weight alternative.
| Artifact | VRAM (GB) | Request p50 (ms) | RoboLab-120 SR (%) |
|---|---|---|---|
| Edge BF16 | 9.20 | 582.0 | 20.92 |
| Edge GenW8A8 | 8.79 | 331.4 | 19.25 |
Quickstart
Install the Hugging Face CLI, clone the runtime, and download this self-contained bundle:
python -m pip install -U "huggingface_hub[cli]"
hf download "XXXXyu/Cosmos3-Edge-Policy-DROID-GenW8A8" --local-dir /data/cosmos_lite/edge_genw8a8
git clone https://github.com/xxxxyu/cosmos-lite.git
cd cosmos-lite
CUDA_VISIBLE_DEVICES=0 examples/robolab_quant/pipeline.sh setup --with-sage
Validate the bundle before loading it:
BUNDLE_DIR=/data/cosmos_lite/edge_genw8a8 \
STRATEGY=gen_branch_w8a8 \
examples/robolab_quant/pipeline.sh validate
For serving and closed-loop RoboLab evaluation, follow the RoboLab pipeline guide.
Quantization And Bundle Details
| Field | Value |
|---|---|
| Strategy | gen_branch_w8a8 |
| W4 / W8A8 generation modules | 252 / 252 (Nano); 168 / 168 (Edge) |
| Activation quantization | Dynamic FP8 only in the generation branch; other activations remain BF16 |
| Calibration | W4 scales from 128 successful DROID training episodes; no RoboLab evaluation data |
Format Note
This is a Cosmos Lite deployment bundle, not a generic GPTQ or AWQ checkpoint. The packed weights, precision map, residual tensors, processor assets, and runtime manifest must be loaded through Cosmos Lite. Weight-only artifacts keep activations in BF16; GenW8A8 artifacts use FP8 W8A8 only in the generation branch and retain the documented BF16 paths elsewhere.
The default server binds to 127.0.0.1:8000 and has no built-in TLS or
authentication. Do not expose it directly to an untrusted network.
Provenance
- Base policy:
nvidia/Cosmos3-Edge-Policy-DROID - Runtime: Cosmos Lite
- Bundle contents include runtime configuration, source revisions, file sizes, and SHA256 hashes.
Limitations And Safety
Simulation results do not establish transfer to a new robot, camera layout, task distribution, or action contract. Validate those changes with paired closed-loop rollouts. This policy is not real-robot safety certified. Real deployment requires an independent E-stop, watchdog, motion limits, stale-command rejection, and operator supervision.
This community quantization is not an NVIDIA product.
License
The upstream model and this derivative are distributed under OpenMDW-1.1. Review the upstream model card and repository notices before use.
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Base model
nvidia/Cosmos3-Edge-Policy-DROID