| --- |
| base_model: Roblox/cube3d-v0.5 |
| base_model_relation: quantized |
| tags: |
| - 3d-generation |
| - text-to-3d |
| - quantized |
| - int4 |
| - torchao |
| - rtn |
| license: openrail |
| library_name: torchao |
| pipeline_tag: text-to-3d |
| --- |
| |
| # 🚀 First INT4 Quantized Efficient-Cube3D - Run on Half the VRAM |
|
|
| [](https://github.com/TrNi/efficient-cube3d) |
| [](https://drive.google.com/file/d/1-mWdiDHJIozQnKC-bP9TmaWR0fQLRo2X/view?usp=sharing) |
|
|
| -blueviolet) |
| -brightgreen) |
| -blue) |
| -yellowgreen) |
|
|
| Presenting the **first INT4 quantized efficient version** of [Cube3D v0.5](https://huggingface.co/Roblox/cube3d-v0.5), a text-to-3D mesh generative model. |
| Quantized via **RTN W4A16** (group_size=128) using [torchao](https://github.com/pytorch/ao), it cuts the model size from **7.2GB → 1.3GB (82%↓)** |
| and peak VRAM from **25.4 GB → 11.3 GB (55%↓)** while maintaining the same inference speed and comparable shape fidelity - |
| enabling 3D shape generation on much smaller, more accessible GPUs. |
| |
| | | BF16 + Engine | BF16 + EngineFast | **INT4 + EngineFast** | |
| |---|:-:|:-:|:-:| |
| | 💾 Model size | 7.17 GB | 7.17 GB | **1.26 GB (82%↓)** | |
| | 🎮 Peak VRAM | 21.7 GB | 25.4 GB | **11.3 GB (55%↓)** ✨ | |
| | 📦 Setup time | 19.4 s | 206.9 s | **6.9 s (97%↓)** | |
| | ⏱️ Latency | 90.9 s | 15.0 s | **14.2 s** | |
| |
| <mark>💡 The 82% size reduction and 55% VRAM reduction means this model now fits on a single 15 GB GPU (e.g. NVIDIA L4, A10, A2 etc.), bringing high-quality text-to-3D generation to individual researchers and end-user hardware. |
| </mark> |
| |
| ### Shaded renders: BF16 vs INT4 |
| |
| | Prompt | BF16 | **INT4** | |
| |---|:-:|:-:| |
| | A pineapple with a crown of 8 spiky leaves and a hexagonal surface pattern |  |  | |
| | steampunk boombox with 2 circular speakers and 6 brass dials |  |  | |
| | A sitting cat with pointed ears and a long tail |  |  | |
| | A lace doily pattern extruded to a thin 3D surface |  |  | |
| |
| Rendered with spherical harmonic lighting, Blinn-Phong specular and ambient occlusion: gray only, no color, so the geometry itself shows. The lace doily is a failure case: INT4 loses the lace pattern. |
| |
| ### Original BF16 vs Quantized INT4 Comparisons: |
| ##### A. Easy Categories (3) |
|  |
| ##### B. Medium Categories (7) |
|  |
| ##### C. Complex Categories (5) |
|  |
| |
| |
| # Cube3D v0.5 - RTN W4A16 INT4 (torchao) |
| |
| Post-training quantized version of [Roblox/cube3d-v0.5](https://huggingface.co/Roblox/cube3d-v0.5), a text-to-3D mesh generative model. |
| Quantization method: **RTN W4A16**, group_size=128, via [torchao](https://github.com/pytorch/ao) `int4_weight_only`. |
|
|
| ## What's in this repo |
|
|
| | File | Size | Description | |
| |------|------|-------------| |
| | `shape_gpt_rtn_int4_g128.pt` | 1.26 GB | INT4 quantized GPT weights (torchao pickle) | |
| | `shape_tokenizer.safetensors` | ~1.10 GB | VQ-VAE decoder — BF16, unchanged from base model | |
| | `open_model_v0.5.yaml` | tiny | Model architecture config | |
| | `quant_config.json` | tiny | Quantization metadata | |
|
|
|
|
| ## New Benchmarking Dataset (15 categories, 310 prompts) |
|
|
| ### Shape Quality (Chamfer Distance, 15 categories, 310 prompts): |
|
|
| Median Chamfer Distance: 67.7 × 10⁻³ |
|
|
| Best categories: `animal_domestic` (55.0), `vehicle_land` (52.2), `architecture` (54.0). |
| Complex categories: `symmetry_topology` (113.6), `abstract_mathematical` (107.2) — high variance. |
|
|
|
|
| | Category | Median | Mean | Std | n | |
| |---|---:|---:|---:|---:| |
| **Easy** (CD × 10⁻³ < 75) |
| | animal_domestic | 55.0 | 60.4 | 25.5 | 20 | |
| | vehicle_land | 52.2 | 61.1 | 39.0 | 20 | |
| | architecture | 54.0 | 61.7 | 29.9 | 20 | |
| **Medium** (CD × 10⁻³ 75–100) |
| | musical_instrument | 43.1 | 79.0 | 86.3 | 20 | |
| | animal_wild | 65.2 | 80.4 | 45.2 | 20 | |
| | geometric_primitive | 40.8 | 81.0 | 90.2 | 20 | |
| | furniture | 74.9 | 82.5 | 39.8 | 20 | |
| | fine_detail | 57.7 | 83.3 | 72.8 | 20 | |
| | original_visuals | 71.6 | 79.5 | 47.3 | 30 | |
| | vehicle_air_water | 77.8 | 97.5 | 78.9 | 20 | |
| **Complex** (CD × 10⁻³ > 100) |
| | electronics | 97.4 | 126.4 | 79.3 | 20 | |
| | nature_plant | 111.3 | 132.0 | 69.9 | 20 | |
| | tool_hardware | 63.7 | 139.5 | 193.7 | 20 | |
| | abstract_mathematical | 107.2 | 147.4 | 124.2 | 20 | |
| | symmetry_topology | 113.6 | 176.5 | 169.5 | 20 | |
| |
| ## Requirements |
| |
| ``` |
| torch==2.10.0+cu128 |
| torchvision==0.25.0+cu128 |
| torchaudio==2.10.0 |
| torchao==0.10.0 |
| ``` |
| |
| The .pt file is a torchao pickle, torchao enables kernel-supported INT4 inference. |
| |
| ## Usage |
| Please see the [](https://drive.google.com/file/d/1-mWdiDHJIozQnKC-bP9TmaWR0fQLRo2X/view?usp=sharing) |
| |
| |
| ## Quantization details |
| |
| - **Method**: Round-to-nearest (RTN) |
| - **Precision**: W4A16 - weights INT4, activations BF16 |
| - **Quantized INT4 layers**: 279 / 282 |
| - **Skipped layers**: `shape_proj` (in_features=16, < group size), `lm_head` (out=4099, output head), `bbox_proj` |
| - **Torchao Quantization Group size**: 128 |
|
|
|
|
| ## Citation |
| ```bibtex |
| @article{roblox2025cube, |
| title={Cube: A Roblox View of 3D Intelligence}, |
| author={Roblox}, |
| journal={arXiv preprint arXiv:2503.15475}, |
| year={2025} |
| } |
| ``` |
|
|
