ASE
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
- NVIDIA,University of California (UC) Berkeley
- Organisation type
- Industry,Academia
- Country
- United States of America
- Published
- 5 May 2022
- Authors
- Xue Bin Peng, Yunrong Guo, Lina Halper, Sergey Levine, Sanja Fidler
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Robotics
- Task
- Animal (human/non-human) imitation
- Approach
- Reinforcement learning
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.
- Training data
- tokens
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 4.5 × 10¹⁹ FLOP
- How it was established
- Hardware
Training was done using the Isaac Gym simulator on an NVIDIA V100 GPU. The model was trained on over 10 billion samples, which equates to 10 years of simulated experience time. Training took around 10 days on a single GPU. 1.3e14 * 10 * 24 * 3600 * 0.4 = 4.49e19
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 Tesla V100 PCIe 16 GB
- Wall-clock time
- 240 hours (10 days)
Training took around 10 days on a single GPU.
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
- Citations
- 56
Sources
Where this record came from and when it was last checked.
- Reference
- ASE: Large-Scale Reusable Adversarial Skill Embeddings for Physically Simulated Characters
- Last updated
- 28 November 2025
What the numbers mean
About this model
ASE was published by NVIDIA,University of California (UC) Berkeley, in the country recorded as United States of America, during May 2022. The publishing organisation is categorised as industry,Academia.
It works in the domain of Robotics, and is recorded as performing the task of animal (human/non-human) imitation.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
The training run consumed about 4.5 × 10¹⁹ FLOP, on hardware recorded as NVIDIA Tesla V100 PCIe 16 GB. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
ASE — common questions
ASE— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
ASE— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
ASE— who created it?
It was published by NVIDIA,University of California (UC) Berkeley, based in United States of America, an organisation categorised as industry,Academia.
ASE— when was it released?
It was published in May 2022. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
ASE— what is it used for?
It works in the domain of Robotics, and is recorded as handling the task of animal (human/non-human) imitation. 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.
ASE— how much compute was used to train it?
Training consumed around 4.5 × 10¹⁹ FLOP, on hardware recorded as NVIDIA Tesla V100 PCIe 16 GB. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
ASE— what GPU do I need to run it?
None. This 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.