METL-Global
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
- University of Wisconsin Madison,Morgridge Institute for Research
- Organisation type
- Academia,Academia
- Country
- United States of America
- Published
- 17 April 2024
- Authors
- Sam Gelman, Bryce Johnson, Chase Freschlin, Sameer D’Costa, Anthony Gitter, Philip A. Romero
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 design
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
- 50M
- Training data
- tokens
- Epochs
- 30
New estimate, Global only: 30M * 200 residues = 6000000000 Combined data 50M variants (20M + 30M) × 200 residues = 10 billion (1.0e10) tokens 20M from METL-Local 30M from METL-Global (148 proteins × 200k variants) Average sequence length: 200 residues Final estimate: 1.0e10 tokens
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Chips used
- 4
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
- 25
Sources
Where this record came from and when it was last checked.
- Reference
- Biophysics-based protein language models for protein engineering
- Last updated
- 1 January 2026
What the numbers mean
About this model
METL-Global was published by University of Wisconsin Madison,Morgridge Institute for Research, in United States of America, in April 2024. academia,Academia is the category the publisher falls under.
It works in Biology, and is recorded as doing protein or nucleotide language model (pLM/nLM), Protein design.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Answers
METL-Global — common questions
What GPU do I need to run METL-Global?
None. METL-Global 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 METL-Global open source?
The licensing for METL-Global was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does METL-Global have?
METL-Global has 50M 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.
Who created METL-Global?
METL-Global was published by University of Wisconsin Madison,Morgridge Institute for Research, based in United States of America, categorised as academia,Academia.
When was METL-Global released?
METL-Global was published in April 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 METL-Global used for?
METL-Global works in Biology, and is recorded as handling protein or nucleotide language model (pLM/nLM), Protein design. 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.
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.