Flow++ (CIFAR10)
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 California (UC) Berkeley,Covariant
- Organisation type
- Academia,Industry
- Country
- United States of America
- Published
- 15 May 2019
- Authors
- Jonathan Ho, Xi Chen, Aravind Srinivas, Yan Duan, 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.
- Parameters
- 31.4M
- Training data
- tokens
- Epochs
- 400
Table 2: 31.4M
Training run for CIFAR10 ablations is ~400 epochs (Table 2)
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Chips used
- 8
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 source
MIT license https://github.com/aravindsrinivas/flowpp
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
- Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design
- Last updated
- 11 February 2026
What the numbers mean
Where it came from
Flow++ (CIFAR10) was published by University of California (UC) Berkeley,Covariant, in the country recorded as United States of America, during May 2019. The category the publisher falls under is academia,Industry.
It works in the domain of Image generation, and is recorded as performing the task of image generation.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
Flow++ (CIFAR10) — common questions
Flow++ (CIFAR10)— who created it?
It was published by University of California (UC) Berkeley,Covariant, based in United States of America, an organisation categorised as academia,Industry.
Flow++ (CIFAR10)— when was it released?
It was published in May 2019. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Flow++ (CIFAR10)— 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.
Flow++ (CIFAR10)— what GPU do I need to run it?
None. This 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.
Flow++ (CIFAR10)— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Flow++ (CIFAR10)— how many parameters does it have?
It has a parameter count of 31.4M. Table 2: 31.4M. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
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.