Layer Normalization: Draw
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 Toronto
- Organisation type
- Academia
- Country
- Canada
- Published
- 21 July 2016
- Authors
- Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton
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
- Draw
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
- 39,200,000 tokens
The dataset has been split into 50,000 training, 10,000 validation and 10,000 test
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
- Layer Normalization
- Last updated
- 28 November 2025
What the numbers mean
What this model is
Layer Normalization: Draw was published by University of Toronto, in Canada, in July 2016. academia is the category the publisher falls under.
It works in Image generation, and is recorded as doing image generation.
Its starting point was Draw — most models at this scale are adapted from an existing base rather than built from nothing.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
It was trained on about 39,200,000 tokens of text.
Answers
Layer Normalization: Draw — common questions
How many parameters does Layer Normalization: Draw have?
No parameter count has been published for Layer Normalization: Draw, which is why no memory or speed figure appears on this page.
Who created Layer Normalization: Draw?
Layer Normalization: Draw was published by University of Toronto, based in Canada, categorised as academia.
When was Layer Normalization: Draw released?
Layer Normalization: Draw was published in July 2016. 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 Layer Normalization: Draw used for?
Layer Normalization: Draw works in Image generation, and is recorded as handling image generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run Layer Normalization: Draw?
None. Layer Normalization: Draw 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 Layer Normalization: Draw open source?
The licensing for Layer Normalization: Draw was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
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.