VISTA-2D
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
- 22 April 2024
- Authors
- Vishwesh Nath, Andriy Myronenko, Harry Clifford
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology, Vision
- Task
- Cell segmentation
- Base model
- Segment Anything Model
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
- 100M
- Training data
- tokens
"VISTA-2D has a network architecture with ~100 million training hyperparameters" Presumably the quote is incorrect and they meant 100 million parameters.
"A total of ~15,000 annotated cell images were collected to train the generalist VISTA-2D model."
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
- Hosted access (no API)
- 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
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Advancing Cell Segmentation and Morphology Analysis with NVIDIA AI Foundation Model VISTA-2D
- Last updated
- 28 November 2025
What the numbers mean
What this model is
VISTA-2D was published by NVIDIA, in the country recorded as United States of America, during April 2024. The category the publisher falls under is industry.
It works in the domain of Biology, Vision, and is recorded as performing the task of cell segmentation.
Rather than being trained from scratch, it is derived from Segment Anything Model. Most models at this scale are adapted from an existing base rather than built from nothing.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Answers
VISTA-2D — common questions
VISTA-2D— when was it released?
It was published in April 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
VISTA-2D— what is it used for?
It works in the domain of Biology, Vision, and is recorded as handling the task of cell segmentation. These are the areas it was designed around; they describe intent rather than a hard boundary.
VISTA-2D— 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.
VISTA-2D— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
VISTA-2D— how many parameters does it have?
It has a parameter count of 100M. "VISTA-2D has a network architecture with ~100 million training hyperparameters" Presumably the quote is incorrect and they meant 100 million parameters. 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.
VISTA-2D— who created it?
It was published by NVIDIA, based in United States of America, an organisation categorised as industry.
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.