NVAE (FFHQ)
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
- 200
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
- 5.2 × 10²⁰ FLOP
- How it was established
- Hardware
125000000000000 FLOP / GPU / sec * 24 GPUs * 160 hours * 3600 sec / hour * 0.3 [assumed utilization] = 5.184e+20 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 32 GB
- Chips used
- 24
- Wall-clock time
- 160 hours
- Power draw
- 12.2 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
Background
NVAE (FFHQ) 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.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
Producing it required around 5.2 × 10²⁰ FLOP of arithmetic, on NVIDIA Tesla V100 DGXS 32 GB, which is a statement about the training budget rather than about inference.
Answers
NVAE (FFHQ) — common questions
How many parameters does NVAE (FFHQ) have?
No parameter count has been published for NVAE (FFHQ), which is why no memory or speed figure appears on this page.
Who created NVAE (FFHQ)?
NVAE (FFHQ) was published by NVIDIA, based in United States of America, categorised as industry.
When was NVAE (FFHQ) released?
NVAE (FFHQ) 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 (FFHQ) used for?
NVAE (FFHQ) works in Image generation, and is recorded as handling image generation. These are the areas it was designed around; they describe intent rather than a hard boundary.
How much compute was used to train NVAE (FFHQ)?
Around 5.2 × 10²⁰ FLOP, on NVIDIA Tesla V100 DGXS 32 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 (FFHQ)?
None. NVAE (FFHQ) 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 (FFHQ) open source?
No. NVAE (FFHQ) has not had its weights published, so it exists only as a service controlled by its owner.
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.