DecompDiff
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 Illinois Urbana-Champaign (UIUC),ByteDance,University of Chinese Academy of Sciences,Chinese Academy of Sciences,Tsinghua University
- Organisation type
- Academia,Industry,Academia,Academia,Academia
- Country
- United States of America, China
- Published
- 26 February 2024
- Authors
- Jiaqi Guan, Xiangxin Zhou, Yuwei Yang, Yu Bao, Jian Peng, Jianzhu Ma, Qiang Liu, Liang Wang, Quanquan Gu
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
- 12,500,000 tokens
"100,000 complexes are selected for training" (diffusion based model)
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 1.9 × 10¹⁹ FLOP
- How it was established
- Hardware
1. Hardware setup: 1x NVIDIA A100 GPU (3.12e14 FLOPs/s for FP16) 2. Training duration: 41.7 hours (provided directly) = 150,120 seconds 3. Utilization rate: 40% (assumed) 4. Calculation: 3.12e14 FLOPs/s × 1 GPU × 150,120s × 0.4 = 1.873e19 FLOPs
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 A100
- Chips used
- 1
- Wall-clock time
- 42 hours
- Power draw
- 435 W
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
- 119
Sources
Where this record came from and when it was last checked.
- Reference
- DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
DecompDiff was published by University of Illinois Urbana-Champaign (UIUC),ByteDance,University of Chinese Academy of Sciences,Chinese Academy of Sciences,Tsinghua University, in the country recorded as United States of America, during February 2024. The category the publisher falls under is academia,Industry,Academia,Academia,Academia.
It works in the domain of Biology, and is recorded as performing the task of drug discovery.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
The training run consumed about 1.9 × 10¹⁹ FLOP, on hardware recorded as NVIDIA A100. That figure measures what producing the model cost, and has no bearing on how fast it answers.
The training set ran to roughly 12,500,000 tokens of text.
Answers
DecompDiff — common questions
DecompDiff— 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.
DecompDiff— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
DecompDiff— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
DecompDiff— who created it?
It was published by University of Illinois Urbana-Champaign (UIUC),ByteDance,University of Chinese Academy of Sciences,Chinese Academy of Sciences,Tsinghua University, based in United States of America, an organisation categorised as academia,Industry,Academia,Academia,Academia.
DecompDiff— when was it released?
It was published in February 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.
DecompDiff— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of drug discovery. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
DecompDiff— how much compute was used to train it?
Training consumed around 1.9 × 10¹⁹ FLOP, on hardware recorded as NVIDIA A100. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
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.