STORM-B/8
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- University of Southern California,Georgia Institute of Technology,Stanford University,NVIDIA
- Organisation type
- Academia,Academia,Academia,Industry
- Country
- United States of America
- Published
- 31 December 2024
- Authors
- Jiawei Yang, Jiahui Huang, Yuxiao Chen, Yan Wang, Boyi Li, Yurong You, Apoorva Sharma, Maximilian Igl, Peter Karkus, Danfei Xu, Boris Ivanovic, Yue Wang, Marco Pavone
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- 3D modeling
- Task
- 3D reconstruction, 3D segmentation
- Numerical format
- TF32
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Parameters
- 100.6M
- Training data
- tokens
- Batch size
- 64
A.3 Baseline Implementations: "The number of trainable parameters of these models are controlled to be similar, i.e., GS-LRM has 86.68M parameters, LGM has 103.29M parameters, while our default STORM has 100.60M parameters. 3.3 Implementation, Model architecture: "By default, we use a 12-layer Vision Transformer (ViT-B) (Dosovitskiy, 2020) with full attention and a patch size of 8, along with M = 16 motion tokens." https://github.com/NVlabs/GaussianSTORM?tab=readme-ov-file#training Training: "…
4 Experiments, Datasets: "We primarily conduct experiments on the Waymo Open Dataset (Sun et al., 2020), which contains 1,000 sequences of driving logs: 798 sequences for training and 202 for validation. Each sequence consists of a 20-second video recorded at 10FPS." 10 frames/s * 20 s/scene = 200 frames/scene 200 frames/scene * 798 scene = 159600 frames (training) scene range: [0.1,20] s, or [1,200] frames Sampling scheme: 3.3 Implementation, Supervision and loss functions: "During traini…
Global batch size is 64; repo suggests per-GPU split of 4
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA A100
- Chips used
- 16
- Power draw
- 12.6 kW
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- SOTA improvement
- Record confidence
- Confident
- Citations
- 38
Abstract: "Extensive experiments on public datasets show that STORM achieves precise dynamic scene reconstruction, surpassing state-of-the-art per-scene optimization methods (+4.3 to 6.6 PSNR) and existing feed-forward approaches (+2.1 to 4.7 PSNR) in dynamic regions. STORM reconstructs large-scale outdoor scenes in 200ms, supports real-time rendering, and outperforms competitors in scene flow estimation, improving 3D EPE by 0.422m and Acc5 by 28.02%." See Tables 1,2,3 - comparisons to EmerNeRF…
Sources
Where this record came from and when it was last checked.
- Reference
- STORM: Spatio-Temporal Reconstruction Model for Large-Scale Outdoor Scenes
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
STORM-B/8 was published by University of Southern California,Georgia Institute of Technology,Stanford University,NVIDIA, in United States of America, in December 2024. academia,Academia,Academia,Industry is the category the publisher falls under.
It works in 3D modeling, and is recorded as doing 3D reconstruction, 3D segmentation.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
STORM-B/8 — common questions
Is STORM-B/8 open source?
The licensing for STORM-B/8 was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does STORM-B/8 have?
STORM-B/8 has 100.6M parameters. A.3 Baseline Implementations: "The number of trainable parameters of these models are controlled to be similar, i.e., GS-LRM has 86.68M parameters, LGM has 103.29M parameters, while our default STORM has 100.60M parameters. 3.3 Implementation, Model architecture: "By default, we use a 12-layer Vision Transformer (ViT-B) (Dosovitskiy, 2020) with full attention and a patch size of 8, along with M = 16 motion tokens." https://github.com/NVlabs/GaussianSTORM?tab=readme-ov-file#training Training: "Multi-GPU example that reproduces the paper's STORM-B/8 model: https://github.com/NVlabs/GaussianSTORM/blob/main/storm/models/storm.py def STORM_B_8(**kwargs): return STORM(patch_size=8, embed_dim=768, depth=12, num_heads=12, **kwargs) def STORM_L_8(**kwargs): return STORM(patch_size=8, embed_dim=1024, depth=24, num_heads=16, **kwargs) def STORM_B_16(**kwargs): return STORM(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs) def STORM_L_16(**kwargs): return STORM(patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs) def STORM_XL_8(**kwargs): return STORM(patch_size=8, embed_dim=1152, depth=28, num_heads=16, **kwargs) def STORM_H_8(**kwargs): return STORM(patch_size=8, embed_dim=1280, depth=32, num_heads=16, **kwargs) def STORM_H_16(**kwargs): return STORM(patch_size=16, embed_dim=1280, depth=32, num_heads=16, **kwargs) PyTorch model.parameters(): with "dummy decoder": STORM-B/8: 100.60M (100,598,707) STORM-L/8: 327.08M (327,081,139) STORM-XL/8: 476.55M (476,547,379) STORM-H/8: 665.91M (665,905,587) STORM-B/16: 103.42M (103,417,907) STORM-L/16: 330.82M (330,817,331) STORM-H/16: 670.56M (670,558,771) with "conv decoder", i.e. "Latent-STORM" (adds ~30M): STORM-B/8: 133.23M (133,230,019) STORM-L/8: 359.13M (359,125,699) STORM-XL/8: 508.30M (508,298,563) STORM-H/8: 697.36M (697,363,395) STORM-B/16: 134.21M (134,211,523) STORM-L/16: 360.43M (360,434,371) STORM-H/16: 699.00M (698,999,235). That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Who created STORM-B/8?
STORM-B/8 was published by University of Southern California,Georgia Institute of Technology,Stanford University,NVIDIA, based in United States of America, categorised as academia,Academia,Academia,Industry.
When was STORM-B/8 released?
STORM-B/8 was published in December 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is STORM-B/8 used for?
STORM-B/8 works in 3D modeling, and is recorded as handling 3D reconstruction, 3D segmentation. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
What GPU do I need to run STORM-B/8?
None. STORM-B/8 is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.