MLDD3UTRmRRNAS
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
- 7 October 2024
- Authors
- Alyssa Kramer Morrow, Ashley Thornal, Elise Duboscq Flynn, Emily Hoelzli, Meimei Shan, Gorkem Garipler, Rory Kirchner, Aniketh Janardhan Reddy, Sophia Tabchouri, Ankit Gupta, Jean-Baptiste Michel, Uri Laserson
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), Nucleotide generation
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
- 44M
- Training data
- tokens
TOKENS = 113,000 × 164 = 18532000
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
- 1
- Power draw
- 433 W
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
"we’re making our generative model used for 3’ UTR design trained on genomic 3’ UTRs available via our API for $0.18 per 1M tokens." https://biopharma.ginkgo.bio/resources/white-papers/engineering-stable-mrna-insights-from-ml-driven-design-of-3-utrs
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
- Citations
- 6
Sources
Where this record came from and when it was last checked.
- Reference
- ML-driven design of 3’ UTRs for mRNA stability
- Last updated
- 1 January 2026
What the numbers mean
Where it came from
MLDD3UTRmRRNAS was published by Ginkgo Bioworks, in United States of America, in October 2024. The organisation is categorised as industry.
It works in Biology, and is recorded as doing protein or nucleotide language model (pLM/nLM), Nucleotide generation.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
MLDD3UTRmRRNAS — common questions
Who created MLDD3UTRmRRNAS?
MLDD3UTRmRRNAS was published by Ginkgo Bioworks, based in United States of America, categorised as industry.
When was MLDD3UTRmRRNAS released?
MLDD3UTRmRRNAS was published in October 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 MLDD3UTRmRRNAS used for?
MLDD3UTRmRRNAS works in Biology, and is recorded as handling protein or nucleotide language model (pLM/nLM), Nucleotide generation. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run MLDD3UTRmRRNAS?
None. MLDD3UTRmRRNAS 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 MLDD3UTRmRRNAS open source?
No. MLDD3UTRmRRNAS has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does MLDD3UTRmRRNAS have?
MLDD3UTRmRRNAS has 44M parameters. 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.
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.