ESMFlow
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
- Massachusetts Institute of Technology (MIT)
- Organisation type
- Academia
- Country
- United States of America
- Published
- 2 September 2024
- Authors
- Bowen Jing, Bonnie Berger, Tommi Jaakkola
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- 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
- 552,960,000 tokens
ESMFLOW: 720K × 256 = 184,320,000 27K × 256 = 6,912,000 Total: 184,320,000 + 6,912,000 = 191,232,000
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
- 7.6 × 10²⁰ FLOP
- How it was established
- Reported
1. Hardware setup: 8x NVIDIA A100 GPUs, 3.12e+14 FLOP/s per GPU 2. Training duration: 371 hours (267h AlphaFLOW + 104h ESMFLOW) = 1,335,600 seconds 3. Utilization rate: 40% 4. Final calculation: 8 GPUs × 3.12e+14 FLOP/s × 1,335,600s × 0.4 = 1.34e+21 FLOPs ESMFlow training time, table 2 104+37+5+34+9+23=212 212hours*8*3.12e14*0.4=761978880000000000000
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
- 8
- Power draw
- 6.3 kW
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
MIT license https://github.com/bjing2016/alphaflow
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
- 237
Sources
Where this record came from and when it was last checked.
- Reference
- AlphaFold Meets Flow Matching for Generating Protein Ensembles
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
ESMFlow was published by Massachusetts Institute of Technology (MIT), in the country recorded as United States of America, during September 2024. The category the publisher falls under is academia.
It works in the domain of Biology, and is recorded as performing the task of protein folding prediction.
Published weights mean the model runs on your machine rather than someone else's, which is what makes the hardware question below answerable at all.
What went into building it
Producing it required arithmetic totalling around 7.6 × 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 552,960,000 tokens of text.
Answers
ESMFlow — common questions
ESMFlow— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of protein folding prediction. 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.
ESMFlow— where can I download it?
The weights are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
ESMFlow— how much compute was used to train it?
Training consumed around 7.6 × 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.
ESMFlow— what GPU do I need to run it?
We cannot say. It 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.
ESMFlow— is it open source?
Its weights are published, so it 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.
ESMFlow— 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.
ESMFlow— who created it?
It was published by Massachusetts Institute of Technology (MIT), based in United States of America, an organisation categorised as academia.
ESMFlow— when was it released?
It was published in September 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.
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.