Alphaflow
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
- tokens
AlphaFLOW: 1.28M × 256 = 327,680,000 43K × 256 = 11,008,000 Total: 327,680,000 + 11,008,000 = 338,688,000
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
- 1.6 × 10²¹ FLOP
- How it was established
- Hardware
Training stages, Table 2: 267+105+11+28+9+39=459 hours 459*60*60*312000000000000*8*0.4=1649756160000000000000
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
About this model
Alphaflow was published by Massachusetts Institute of Technology (MIT), in United States of America, in September 2024. It comes out of academia.
It works in Biology, and is recorded as doing protein folding prediction.
Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded.
What went into building it
Training it took roughly 1.6 × 10²¹ FLOP of computation, on NVIDIA A100 — a measure of what producing the model cost, not of how fast it answers.
Answers
Alphaflow — common questions
How many parameters does Alphaflow have?
No parameter count has been published for Alphaflow, which is why no memory or speed figure appears on this page.
Who created Alphaflow?
Alphaflow was published by Massachusetts Institute of Technology (MIT), based in United States of America, categorised as academia.
When was Alphaflow released?
Alphaflow 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.
What is Alphaflow used for?
Alphaflow works in Biology, and is recorded as handling protein folding prediction. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Where can I download Alphaflow?
The weights for Alphaflow 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 Alphaflow?
Around 1.6 × 10²¹ FLOP, on 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.
What GPU do I need to run Alphaflow?
We cannot say. Alphaflow 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 Alphaflow open source?
Its weights are published, so Alphaflow 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.
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.