NVAE (CIFAR 10)
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
- Organisation type
- Industry
- Country
- United States of America
- Published
- 8 January 2021
- Authors
- Arash Vahdat, Jan Kautz
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Image generation
- Task
- Image generation
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
- Epochs
- 400
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
- 5.9 × 10¹⁹ FLOP
- How it was established
- Hardware
125000000000000 FLOP / GPU / sec * 8 GPUs * 55 hours * 3600 sec / hour * 0.3 [assumed utilization] = 5.94e+19 FLOP [Table 6]
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 DGXS 16 GB
- Chips used
- 8
- Wall-clock time
- 55 hours
- Power draw
- 4.1 kW
Table 6
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Open (non-commercial)
"NVAE may be used non-commercially, meaning for research or evaluation purposes only" https://github.com/NVlabs/NVAE
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- NVAE: A Deep Hierarchical Variational Autoencoder
- Last updated
- 11 February 2026
What the numbers mean
Where it came from
NVAE (CIFAR 10) was published by NVIDIA, in United States of America, in January 2021. The organisation is categorised as industry.
It works in Image generation, and is recorded as doing image generation.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
Training it took roughly 5.9 × 10¹⁹ FLOP of computation, on NVIDIA Tesla V100 DGXS 16 GB — a measure of what producing the model cost, not of how fast it answers.
Answers
NVAE (CIFAR 10) — common questions
How much compute was used to train NVAE (CIFAR 10)?
Around 5.9 × 10¹⁹ FLOP, on NVIDIA Tesla V100 DGXS 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.
What GPU do I need to run NVAE (CIFAR 10)?
None. NVAE (CIFAR 10) 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.
Is NVAE (CIFAR 10) open source?
No. NVAE (CIFAR 10) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does NVAE (CIFAR 10) have?
No parameter count has been published for NVAE (CIFAR 10), which is why no memory or speed figure appears on this page.
Who created NVAE (CIFAR 10)?
NVAE (CIFAR 10) was published by NVIDIA, based in United States of America, categorised as industry.
When was NVAE (CIFAR 10) released?
NVAE (CIFAR 10) was published in January 2021. 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 NVAE (CIFAR 10) used for?
NVAE (CIFAR 10) works in Image generation, and is recorded as handling image generation. 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.
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.