PixelSNAIL (CIFAR 10)
No estimate
No hardware requirements for this model
This model's weights are open, but no parameter count has been published for it. Every memory and speed figure starts from that number, so we would rather show nothing than a fabricated estimate.
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 California (UC) Berkeley
- Organisation type
- Academia
- Country
- United States of America
- Published
- 28 December 2017
- Authors
- Xi Chen, Nikhil Mishra, Mostafa Rohaninejad, Pieter Abbeel
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
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
- Open — downloadable
- Model access
- Open weights (unrestricted)
- Training code
- Open source
MIT license https://github.com/neocxi/pixelsnail-public
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
- PixelSNAIL: An Improved Autoregressive Generative Model
- Last updated
- 11 February 2026
What the numbers mean
About this model
PixelSNAIL (CIFAR 10) was published by University of California (UC) Berkeley, in the country recorded as United States of America, during December 2017. It comes out of an organisation categorised as academia.
It works in the domain of Image generation, and is recorded as performing the task of image generation.
The weights being open is what puts this page in the calculator rather than only in the catalogue: it is a model you can actually hold.
Answers
PixelSNAIL (CIFAR 10) — common questions
PixelSNAIL (CIFAR 10)— 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.
PixelSNAIL (CIFAR 10)— who created it?
It was published by University of California (UC) Berkeley, based in United States of America, an organisation categorised as academia.
PixelSNAIL (CIFAR 10)— when was it released?
It was published in December 2017. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
PixelSNAIL (CIFAR 10)— what is it used for?
It works in the domain of Image generation, and is recorded as handling the task of 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.
PixelSNAIL (CIFAR 10)— where can I download it?
The weights are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
PixelSNAIL (CIFAR 10)— what GPU do I need to run it?
We cannot say. It has open weights, but no parameter count has been published for it, and every memory and speed calculation starts from that number. We would rather show nothing than a fabricated estimate.
PixelSNAIL (CIFAR 10)— is it open source?
Its weights are published, so it can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.
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.