AlphaFold
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
- DeepMind
- Organisation type
- Industry
- Country
- United Kingdom of Great Britain and Northern Ireland
- Published
- 15 January 2020
- Authors
- Andrew W. Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander W. R. Nelson, Alex Bridgland, Hugo Penedones, Stig Petersen, Karen Simonyan, Steve Crossan, Pushmeet Kohli, David T. Jones, David Silver, Koray Kavukcuoglu, Demis Hassabis
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein folding prediction, Proteins
- Approach
- Self-supervised learning
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
- 16.3M
- Training data
- 6,622,252,080 tokens
"Neural network hyperparameters" section of https://www.nature.com/articles/s41586-019-1923-7: “7 × 4 Blocks with 256 channels, cycling through dilations 1, 2, 4, 8” “48 × 4 Blocks with 128 channels, cycling through dilations 1, 2, 4, 8” "Distogram prediction" section: "For the final layer, a position-specific bias was used" Extended Data Fig.1 (b): Shows that each block consists of 9 layers: (1) Batch norm (2) Elu (3) Project down (halves number of dimensions) (4) Batch norm (5) Elu (6) 3x3 …
Training Domains: 29,427 Average Residues per Domain: 100 Data Points per Domain: 100 × 100 = 10,000 Total Data Points = 29,427 × 10,000 = 294,270,000 ≈ 3.0 × 10⁸
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 × 10²⁰ FLOP
- How it was established
- Hardware,Third-party estimation
Estimated in the blogpost below https://www.lesswrong.com/posts/wfpdejMWog4vEDLDg/ai-and-compute-trend-isn-t-predictive-of-what-is-happening "AlphaFold: they say they trained on GPU and not TPU. Assuming V100 GPU, it's 5 days * 24 hours/day * 3600 sec/hour * 8 V100 GPU * 100*10^12 FLOP/s * 33% actual GPU utilization = 10^20 FLOP."
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Wall-clock time
- 120 hours
"Training time: about 5 days for 600,000 steps"
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
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- SOTA improvement,Highly cited
- Record confidence
- Speculative
- Citations
- 2,773
"AlphaFold represents a considerable advance in protein-structure prediction." [Abstract]
Sources
Where this record came from and when it was last checked.
- Reference
- Improved protein structure prediction using potentials from deep learning
- Last updated
- 28 November 2025
What the numbers mean
What this model is
AlphaFold was published by DeepMind, in the country recorded as United Kingdom of Great Britain and Northern Ireland, during January 2020. The publishing organisation is categorised as industry.
It works in the domain of Biology, and is recorded as performing the task of protein folding prediction, Proteins.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
Training it took a computation budget of roughly 1 × 10²⁰ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
The training set ran to roughly 6,622,252,080 tokens of text.
It is tracked in the underlying dataset for one reason in particular: sOTA improvement,Highly cited.
Answers
AlphaFold — common questions
AlphaFold— how much compute was used to train it?
Training consumed around 1 × 10²⁰ FLOP. 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.
AlphaFold— 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.
AlphaFold— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
AlphaFold— how many parameters does it have?
It has a parameter count of 16.3M. "Neural network hyperparameters" section of https://www.nature.com/articles/s41586-019-1923-7: “7 × 4 Blocks with 256 channels, cycling through dilations 1, 2, 4, 8” “48 × 4 Blocks with 128 channels, cycling through dilations 1, 2, 4, 8” "Distogram prediction" section: "For the final layer, a position-specific bias was used" Extended Data Fig.1 (b): Shows that each block consists of 9 layers: (1) Batch norm (2) Elu (3) Project down (halves number of dimensions) (4) Batch norm (5) Elu (6) 3x3 kernel with dilation (7) Batch norm (8) Elu (9) Project up (doubles number of dimensions) Dilations don't change the number of parameters in each filter Assuming that projection layers are convolutional layers with 1x1 kernels Parameter estimate for each layer in a 256 channel block: (1) 256*2 = 512 (2) 0 (3) 1*1*256*128 = 32768 (4) 128*2 = 256 (5) 0 (6) 3*3*128*128 = 147456 (7) 128*2 = 256 (8) 0 (9) 1*1*128*256 + 256 = 33024 Total = 214272 Parameter estimate for each layer in a 128 channel block: (1) 128*2 = 256 (2) 0 (3) 1*1*128*64 = 8192 (4) 64*2 = 128 (5) 0 (6) 3*3*64*64 = 36864 (7) 64*2 = 128 (8) 0 (9) 1*1*64*128 + 128 = 8320 Total = 53897 Estimate total network = 7*4*214272 + 48*4*53897 = 5992616 + 10348224 = 16340840 ~ 16e6 Within a factor of 2 of the estimate of 21M parameters stated in: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7305407/ [Previous approximation: 7 * 4 * 256 * 3 * 3 * 256 + 48 * 4 * 128 * 3 * 3 * 128 = 44826624]. 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.
AlphaFold— who created it?
It was published by DeepMind, based in United Kingdom of Great Britain and Northern Ireland, an organisation categorised as industry.
AlphaFold— when was it released?
It was published in January 2020. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
AlphaFold— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of protein folding prediction, Proteins. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
The other direction
Looking at it from the other side?
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