Uni-Mol Molecular Model
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
- Renmin University of China,DP Technology,AI for Science Institute, Beijing (AISI)
- Organisation type
- Academia,Industry,Government
- Country
- China
- Published
- 6 March 2023
- Authors
- Gengmo Zhou, Zhifeng Gao, Qiankun Ding, Hang Zheng, Hongteng Xu, Zhewei Wei, Linfeng Zhang, Guolin Ke
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Molecular representation learning, Molecular property prediction, Protein-ligand contact prediction, 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
"a molecular model pretrained by 209M molecular conformations" Table 6: batch size 128 Max training steps 1M
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
- 5.4 × 10¹⁸ FLOP
- How it was established
- Hardware
31330000000000 FLOP / sec / GPU [fp16 asssumed] * 8 GPUs * 20 hours * 3600 sec / hour * 0.3 [assumed utilization] = 5.413824e+18 FLOP
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 Tesla V100 DGXS 32 GB
- Chips used
- 8
- Wall-clock time
- 20 hours
- Power draw
- 4.0 kW
"Molecular pretraining runs on 8 V100 GPUs (32GB memory, the same below), and the training time is about 20 hours."
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
https://github.com/deepmodeling/Uni-Mol MIT license
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
- Uni-Mol: A Universal 3D Molecular Representation Learning Framework
- Last updated
- 28 November 2025
What the numbers mean
What this model is
Uni-Mol Molecular Model was published by Renmin University of China,DP Technology,AI for Science Institute, Beijing (AISI), in China, in March 2023. The organisation is categorised as academia,Industry,Government.
It works in Biology, and is recorded as doing molecular representation learning, Molecular property prediction, Protein-ligand contact prediction, Drug discovery.
Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded.
Training and provenance
The training run consumed about 5.4 × 10¹⁸ FLOP, on NVIDIA Tesla V100 DGXS 32 GB. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Answers
Uni-Mol Molecular Model — common questions
What is Uni-Mol Molecular Model used for?
Uni-Mol Molecular Model works in Biology, and is recorded as handling molecular representation learning, Molecular property prediction, Protein-ligand contact prediction, Drug discovery. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Where can I download Uni-Mol Molecular Model?
The weights for Uni-Mol Molecular Model are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
How much compute was used to train Uni-Mol Molecular Model?
Around 5.4 × 10¹⁸ FLOP, on NVIDIA Tesla V100 DGXS 32 GB. 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.
What GPU do I need to run Uni-Mol Molecular Model?
We cannot say. Uni-Mol Molecular Model 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 Uni-Mol Molecular Model open source?
Its weights are published, so Uni-Mol Molecular Model 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 Uni-Mol Molecular Model have?
No parameter count has been published for Uni-Mol Molecular Model, which is why no memory or speed figure appears on this page.
Who created Uni-Mol Molecular Model?
Uni-Mol Molecular Model was published by Renmin University of China,DP Technology,AI for Science Institute, Beijing (AISI), based in China, categorised as academia,Industry,Government.
When was Uni-Mol Molecular Model released?
Uni-Mol Molecular Model was published in March 2023. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
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.