AutoDiff
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
- Galixir Technologies,Rensselaer Polytechnic Institute,Massachusetts Institute of Technology (MIT)
- Organisation type
- Industry,Academia,Academia
- Country
- China, United States of America
- Published
- 2 April 2024
- Authors
- Xinze Li, Penglei Wang, Tianfan Fu, Wenhao Gao, Chengtao Li, Leilei Shi, Junhong Liu
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Drug discovery
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
22.5M poses = 22.5M datapoints = 22,500,000 datapoints Final estimate = 22.5M
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
- 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
- Citations
- 5
Sources
Where this record came from and when it was last checked.
- Reference
- AUTODIFF: Autoregressive Diffusion Modeling for Structure-based Drug Design
- Last updated
- 28 November 2025
What the numbers mean
About this model
AutoDiff was published by Galixir Technologies,Rensselaer Polytechnic Institute,Massachusetts Institute of Technology (MIT), in China, in April 2024. It comes out of industry,Academia,Academia.
It works in Biology, and is recorded as doing drug discovery.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
AutoDiff — common questions
Is AutoDiff open source?
No. AutoDiff has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does AutoDiff have?
No parameter count has been published for AutoDiff, which is why no memory or speed figure appears on this page.
Who created AutoDiff?
AutoDiff was published by Galixir Technologies,Rensselaer Polytechnic Institute,Massachusetts Institute of Technology (MIT), based in China, categorised as industry,Academia,Academia.
When was AutoDiff released?
AutoDiff 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.
What is AutoDiff used for?
AutoDiff works in Biology, and is recorded as handling drug discovery. 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.
What GPU do I need to run AutoDiff?
None. AutoDiff 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.
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.