ESM-AA
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
- Peking University,Nanjing University,Tsinghua University,PharMolix
- Organisation type
- Academia,Academia,Academia,Industry
- Country
- China
- Published
- 5 April 2024
- Authors
- Kangjie Zheng, Siyu Long, Tianyu Lu, Junwei Yang, Xinyu Dai, Ming Zhang, Zaiqing Nie, Wei-Ying Ma, Hao Zhou
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein folding prediction, Protein or nucleotide language model (pLM/nLM), Proteins
- Base model
- ESM2-35M
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.
- Parameters
- 35M
- Training data
- 1,143,750,000 tokens
Assuming 300 tokens per protein and 100 token per molecule 8M*300+209M*100=23300000000
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
- 7.3 × 10²⁰ FLOP
- How it was established
- Hardware
1. Hardware: 16x NVIDIA A100 GPUs (3.12e14 FLOP/s per GPU) 2. Training duration: 3 days directly reported = 259,200 seconds 3. Utilization: 40% assumed 4. Calculation: 3.12e14 FLOP/s × 16 GPUs × 259,200s × 0.40 = 5.18e20 FLOPs Base model: 209999999999999970000 Total: 209999999999999970000+5.18e20=728000000000000000000
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
- 16
- Wall-clock time
- 72 hours
- Power draw
- 12.7 kW
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
- 19
Sources
Where this record came from and when it was last checked.
- Reference
- ESM All-Atom: Multi-scale Protein Language Model for Unified Molecular Modeling
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
ESM-AA was published by Peking University,Nanjing University,Tsinghua University,PharMolix, in the country recorded as China, during April 2024. The category the publisher falls under is academia,Academia,Academia,Industry.
It works in the domain of Biology, and is recorded as performing the task of protein folding prediction, Protein or nucleotide language model (pLM/nLM), Proteins.
Its starting point was an existing base model, ESM2-35M. That is why it shares the base model's general shape and size.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
The training run consumed about 7.3 × 10²⁰ FLOP, on hardware recorded as NVIDIA A100. That figure measures what producing the model cost, and has no bearing on how fast it answers.
It was trained on a corpus of about 1,143,750,000 tokens of text.
Answers
ESM-AA — common questions
ESM-AA— how many parameters does it have?
It has a parameter count of 35M. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
ESM-AA— who created it?
It was published by Peking University,Nanjing University,Tsinghua University,PharMolix, based in China, an organisation categorised as academia,Academia,Academia,Industry.
ESM-AA— when was it released?
It 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.
ESM-AA— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of protein folding prediction, Protein or nucleotide language model (pLM/nLM), Proteins. These are the areas it was designed around; they describe intent rather than a hard boundary.
ESM-AA— how much compute was used to train it?
Training consumed around 7.3 × 10²⁰ 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.
ESM-AA— 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.
ESM-AA— 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.
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.