Odyssey 102B
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
- Anthrogen
- Organisation type
- Industry
- Country
- United States of America
- Published
- 18 October 2025
- Authors
- Ankit Singhal, Shyam Venkatasubramanian, Sean Moushegian, Steven Strutt, Michael Lin, Connor Lee
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), Protein 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
- 102B
- Training data
- tokens
102B
Table 2 3.662B proteins
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
- 1.1 × 10²³ FLOP
- How it was established
- Reported
"trained over 1.1 × 10^23 FLOPs" from the abstract
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.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Odyssey: reconstructing evolution through emergent consensus in the global proteome
- Last updated
- 28 November 2025
What the numbers mean
About this model
Odyssey 102B was published by Anthrogen, in United States of America, in October 2025. It comes out of industry.
It works in Biology, and is recorded as doing protein or nucleotide language model (pLM/nLM), Protein generation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
Producing it required around 1.1 × 10²³ FLOP of arithmetic, which is a statement about the training budget rather than about inference.
Answers
Odyssey 102B — common questions
Who created Odyssey 102B?
Odyssey 102B was published by Anthrogen, based in United States of America, categorised as industry.
When was Odyssey 102B released?
Odyssey 102B was published in October 2025.
What is Odyssey 102B used for?
Odyssey 102B works in Biology, and is recorded as handling protein or nucleotide language model (pLM/nLM), Protein generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train Odyssey 102B?
Around 1.1 × 10²³ 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.
What GPU do I need to run Odyssey 102B?
None. Odyssey 102B 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 Odyssey 102B open source?
No. Odyssey 102B has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Odyssey 102B have?
Odyssey 102B has 102B parameters. 102B. 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.