life2vec
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
- Technical University of Denmark,University of Copenhagen
- Organisation type
- Academia,Academia
- Country
- Denmark
- Published
- 5 June 2023
- Authors
- Germans Savcisens, Tina Eliassi-Rad, Lars Kai Hansen, Laust Mortensen, Lau Lilleholt, Anna Rogers, Ingo Zettler, Sune Lehmann
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Medicine
- Task
- Mortality 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.
- Parameters
- 8.4M
- Training data
- 3,252,086 tokens
Appendix B: 8.4m
The total number of residents in the filtered dataset is 3 252 086.
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
- 1.6 × 10¹⁴ FLOP
- How it was established
- Operation counting
=6*3252086*8400000=1.639051344 × 10^14 I am not sure about datasize thus Speculative confidence level
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
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Speculative
Sources
Where this record came from and when it was last checked.
- Reference
- Using Sequences of Life-events to Predict Human Lives
- Last updated
- 28 November 2025
What the numbers mean
What this model is
life2vec was published by Technical University of Denmark,University of Copenhagen, in Denmark, in June 2023. It comes out of academia,Academia.
It works in Medicine, and is recorded as doing mortality prediction.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
Training it took roughly 1.6 × 10¹⁴ FLOP of computation — a measure of what producing the model cost, not of how fast it answers.
It was trained on about 3,252,086 tokens of text.
Answers
life2vec — common questions
How much compute was used to train life2vec?
Around 1.6 × 10¹⁴ 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 life2vec?
None. life2vec 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 life2vec open source?
No. life2vec has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does life2vec have?
life2vec has 8.4M parameters. Appendix B: 8.4m. 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 life2vec?
life2vec was published by Technical University of Denmark,University of Copenhagen, based in Denmark, categorised as academia,Academia.
When was life2vec released?
life2vec was published in June 2023. 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 life2vec used for?
life2vec works in Medicine, and is recorded as handling mortality prediction. These are the areas it was designed around; they describe intent rather than a hard boundary.
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.