AminoAcid-0
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
- Ginkgo Bioworks
- Organisation type
- Industry
- Country
- United States of America
- Published
- 17 September 2024
- Authors
- Zachary Kurtz, Matt Chamberlin, Eric Danielson, Alex Carlin, Michal Jastrzebski, Dana Merrick, Dmitriy Ryaboy, Emily Wrenbeck, Ankit Gupta
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein or nucleotide language model (pLM/nLM)
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
"Here we investigated the impact of training data on the performance of a protein sequence LLM. We supplemented the ~60M UniRef50 sequence clusters used to train ESM-2 with an additional 112M clusters from the Ginkgo UMDB." Assuming 1 protein per cluster and 300 amino acid tokens per protein. Prediction targets: 172000000 * 300 = 51600000000
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
- API access
- Training code
- Unreleased
API: https://ai.ginkgo.bio/resources/blog/ginkgo-model-api-ai-research
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
Sources
Where this record came from and when it was last checked.
- Reference
- A Protein Sequence LLM Trained on 2 Billion Proprietary Sequences
- Last updated
- 28 November 2025
What the numbers mean
What this model is
AminoAcid-0 was published by Ginkgo Bioworks, in United States of America, in September 2024. The organisation is categorised as industry.
It works in Biology, and is recorded as doing protein or nucleotide language model (pLM/nLM).
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Answers
AminoAcid-0 — common questions
What is AminoAcid-0 used for?
AminoAcid-0 works in Biology, and is recorded as handling protein or nucleotide language model (pLM/nLM). These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run AminoAcid-0?
None. AminoAcid-0 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 AminoAcid-0 open source?
No. AminoAcid-0 has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does AminoAcid-0 have?
No parameter count has been published for AminoAcid-0, which is why no memory or speed figure appears on this page.
Who created AminoAcid-0?
AminoAcid-0 was published by Ginkgo Bioworks, based in United States of America, categorised as industry.
When was AminoAcid-0 released?
AminoAcid-0 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.