MoLFormer-XL
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
- IBM
- Organisation type
- Industry
- Country
- United States of America
- Published
- 25 January 2023
- Authors
- Jerret Ross, Brian Belgodere, Vijil Chenthamarakshan, Inkit Padhi, Youssef Mroueh, Payel Das
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein generation, Protein folding prediction
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
- Epochs
- 4
"MoLFormer-XL has been pretrained on 1.1 billion molecules represented as machine-readable strings of text." 15K steps "PubChem+ZINC (>1 billion data points) datasets" mean token amount (table 4 from supplementary materials): 44.76 44.76*1.1*10^9 = 49236000000 tokens
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
- 4.5 × 10²⁰ FLOP
- How it was established
- Hardware
125000000000000 FLOP / GPU / sec *(208 hours * 16 GPUs +1 GPU * 12 hours) [see training time notes] * 3600 sec / hour * 0.3 [assumed utilization] = 4.509e+20 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 V100
- Chips used
- 16
- Wall-clock time
- 208 hours (8.7 days)
- Power draw
- 9.6 kW
"Together, both techniques raised our per-GPU processing costs from 50 molecules to 1,600 molecules, allowing us to get away with 16 GPUs instead of 1,000. By eliminating hundreds of unnecessary GPUs, we consumed 61 times less energy and still had a trained model in five days. " "Our pretraining task consists of training on the full dataset to 4 epochs. Training a single epoch of just PubChem on a single NVIDIA V100 GPU would take approximately 60 hours. Utilizing Distributed Data Parallel, pre…
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
Python codes for MoLFormer training and fine-tuning, and Python notebooks for MoLFormer attention visualization, as well as instances of pretrained models, are available at https://github.com/IBM/molformer. Apache 2.0
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
- An AI foundation model that learns the grammar of molecules
- Last updated
- 28 November 2025
What the numbers mean
About this model
MoLFormer-XL was published by IBM, in United States of America, in January 2023. It comes out of industry.
It works in Biology, and is recorded as doing protein generation, Protein folding prediction.
The weights being open is what puts this page in the calculator rather than only in the catalogue: it is a model you can actually hold.
Training and provenance
The training run consumed about 4.5 × 10²⁰ FLOP, on NVIDIA V100. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
Answers
MoLFormer-XL — common questions
What is MoLFormer-XL used for?
MoLFormer-XL works in Biology, and is recorded as handling protein generation, Protein folding prediction. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Where can I download MoLFormer-XL?
The weights for MoLFormer-XL 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 MoLFormer-XL?
Around 4.5 × 10²⁰ FLOP, on NVIDIA V100. 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 MoLFormer-XL?
We cannot say. MoLFormer-XL 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 MoLFormer-XL open source?
Its weights are published, so MoLFormer-XL 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 MoLFormer-XL have?
No parameter count has been published for MoLFormer-XL, which is why no memory or speed figure appears on this page.
Who created MoLFormer-XL?
MoLFormer-XL was published by IBM, based in United States of America, categorised as industry.
When was MoLFormer-XL released?
MoLFormer-XL was published in January 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.