ELMo
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 Washington,Allen Institute for AI
- Organisation type
- Academia,Research collective
- Country
- United States of America
- Published
- 1 February 2018
- Authors
- Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, Luke Zettlemoyer
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Question answering, Sentiment classification, Language modeling
- Approach
- Self-supervised learning
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
- 94M
- Training data
- 2,000,000,000 tokens
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
- 3.3 × 10¹⁵ FLOP
- How it was established
- Third-party estimation
3300e12 - https://github.com/amirgholami/ai_and_memory_wall
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Highly cited
- Record confidence
- Speculative
- Citations
- 12,156
Sources
Where this record came from and when it was last checked.
- Reference
- Deep contextualized word representations
- Last updated
- 25 May 2026
What the numbers mean
Background
ELMo was published by University of Washington,Allen Institute for AI, in United States of America, in February 2018. academia,Research collective is the category the publisher falls under.
It works in Language, and is recorded as doing question answering, Sentiment classification, Language modeling.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
Producing it required around 3.3 × 10¹⁵ FLOP of arithmetic, which is a statement about the training budget rather than about inference.
The training set ran to roughly 2,000,000,000 tokens.
The reason it appears in this catalogue at all is highly cited.
Answers
ELMo — common questions
What GPU do I need to run ELMo?
None. ELMo 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 ELMo open source?
The licensing for ELMo 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 ELMo have?
ELMo has 94M 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 ELMo?
ELMo was published by University of Washington,Allen Institute for AI, based in United States of America, categorised as academia,Research collective.
When was ELMo released?
ELMo was published in February 2018. 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 ELMo used for?
ELMo works in Language, and is recorded as handling question answering, Sentiment classification, Language modeling. 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.
How much compute was used to train ELMo?
Around 3.3 × 10¹⁵ 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.
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.