HiPPO-LegS
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
- Stanford University,University at Buffalo
- Organisation type
- Academia,Academia
- Country
- United States of America
- Published
- 23 October 2020
- Authors
- Albert Gu, Tri Dao, Stefano Ermon, Atri Rudra, Christopher Re
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image classification
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
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA P100
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
apache 2.0 https://github.com/state-spaces/s4
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
- HiPPO: Recurrent Memory with Optimal Polynomial Projections
- Last updated
- 28 November 2025
What the numbers mean
About this model
HiPPO-LegS was published by Stanford University,University at Buffalo, in United States of America, in October 2020. It comes out of academia,Academia.
It works in Vision, and is recorded as doing image classification.
Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded.
Answers
HiPPO-LegS — common questions
Who created HiPPO-LegS?
HiPPO-LegS was published by Stanford University,University at Buffalo, based in United States of America, categorised as academia,Academia.
When was HiPPO-LegS released?
HiPPO-LegS was published in October 2020. 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 HiPPO-LegS used for?
HiPPO-LegS works in Vision, and is recorded as handling image classification. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Where can I download HiPPO-LegS?
The weights for HiPPO-LegS are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
What GPU do I need to run HiPPO-LegS?
We cannot say. HiPPO-LegS 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.
Is HiPPO-LegS open source?
Its weights are published, so HiPPO-LegS 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.
How many parameters does HiPPO-LegS have?
No parameter count has been published for HiPPO-LegS, which is why no memory or speed figure appears on this page.
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.