NCP-VAE (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
- University of Illinois Urbana-Champaign (UIUC),NVIDIA
- Organisation type
- Academia,Industry
- Country
- United States of America
- Published
- 3 November 2021
- Authors
- Jyoti Aneja, Alexander Schwing, Jan Kautz, Arash Vahdat
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Image generation
- Task
- Image generation
- Base model
- NVAE (CIFAR 10)
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
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
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
Sources
Where this record came from and when it was last checked.
- Reference
- A Contrastive Learning Approach for Training Variational Autoencoder Priors
- Last updated
- 11 February 2026
What the numbers mean
Background
NCP-VAE (CIFAR 10) was published by University of Illinois Urbana-Champaign (UIUC),NVIDIA, in United States of America, in November 2021. The organisation is categorised as academia,Industry.
It works in Image generation, and is recorded as doing image generation.
Its starting point was NVAE (CIFAR 10) — most models at this scale are adapted from an existing base rather than built from nothing.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
NCP-VAE (CIFAR 10) — common questions
Is NCP-VAE (CIFAR 10) open source?
No. NCP-VAE (CIFAR 10) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does NCP-VAE (CIFAR 10) have?
No parameter count has been published for NCP-VAE (CIFAR 10), which is why no memory or speed figure appears on this page.
Who created NCP-VAE (CIFAR 10)?
NCP-VAE (CIFAR 10) was published by University of Illinois Urbana-Champaign (UIUC),NVIDIA, based in United States of America, categorised as academia,Industry.
When was NCP-VAE (CIFAR 10) released?
NCP-VAE (CIFAR 10) was published in November 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 NCP-VAE (CIFAR 10) used for?
NCP-VAE (CIFAR 10) 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.
What GPU do I need to run NCP-VAE (CIFAR 10)?
None. NCP-VAE (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.
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.