HelixFold
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
- Baidu
- Organisation type
- Industry
- Country
- China
- Published
- 17 May 2024
- Authors
- Xiaomin Fang, Jie Gao, Jing Hu, Lihang Liu, Yang Xue, Xiaonan Zhang, Kunrui Zhu
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
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
- 123 hours
"The complete training time are optimized from 11 days to 5.12 days"
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
- Open (non-commercial)
CC BY-NC-SA 4.0 https://github.com/PaddlePaddle/PaddleHelix
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
- Citations
- 12
Sources
Where this record came from and when it was last checked.
- Reference
- HelixFold-Multimer: Elevating Protein Complex Structure Prediction to New Heights
- Last updated
- 25 May 2026
What the numbers mean
What this model is
HelixFold was published by Baidu, in China, in May 2024. industry is the category the publisher falls under.
It works in Biology, and is recorded as doing protein folding prediction.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
HelixFold — common questions
How many parameters does HelixFold have?
No parameter count has been published for HelixFold, which is why no memory or speed figure appears on this page.
Who created HelixFold?
HelixFold was published by Baidu, based in China, categorised as industry.
When was HelixFold released?
HelixFold was published in May 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 HelixFold used for?
HelixFold works in Biology, and is recorded as handling 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.
What GPU do I need to run HelixFold?
None. HelixFold 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.
Is HelixFold open source?
No. HelixFold has not had its weights published, so it exists only as a service controlled by its owner.
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.