EXAONE 1.0
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
- LG
- Organisation type
- Industry
- Country
- Korea (Republic of)
- Published
- 14 December 2021
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Multimodal, Language, Vision
- Task
- Translation, Language modeling/generation, Visual question answering
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
- 300B
- Training data
- 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
- 1.7 × 10²⁴ FLOP
- How it was established
- Operation counting
No indication of how images are processed. Supposing they used something like ViT-H/14, and training images were 512x512 (they state "EXAONE shows remarkable performance such as [...] offering 1024x1024 sized image output", but typically this size of image training would only be done during a relatively short, final stage of pre-training), there would be 37x37 = 1,369 patches per image 1,369 * 250 million = around 342 billion image patch embeddings. 300M parameters * (342 billion + 600 billion)…
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Compute cost
- $2,702,636
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.
- Frontier model
- Yes
- Likely above 10²³ FLOP
- Yes
- Why it is tracked
- Training cost
- Record confidence
- Speculative
Sources
Where this record came from and when it was last checked.
- Last updated
- 1 December 2025
What the numbers mean
About this model
EXAONE 1.0 was published by LG, in Korea (Republic of), in December 2021. industry is the category the publisher falls under.
It works in Multimodal, Language, Vision, and is recorded as doing translation, Language modeling/generation, Visual question answering.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
The training run consumed about 1.7 × 10²⁴ FLOP. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
The reason it appears in this catalogue at all is training cost.
Answers
EXAONE 1.0 — common questions
How many parameters does EXAONE 1.0 have?
EXAONE 1.0 has 300B 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 EXAONE 1.0?
EXAONE 1.0 was published by LG, based in Korea (Republic of), categorised as industry.
When was EXAONE 1.0 released?
EXAONE 1.0 was published in December 2021. 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 EXAONE 1.0 used for?
EXAONE 1.0 works in Multimodal, Language, Vision, and is recorded as handling translation, Language modeling/generation, Visual question answering. These are the areas it was designed around; they describe intent rather than a hard boundary.
How much compute was used to train EXAONE 1.0?
Around 1.7 × 10²⁴ 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 EXAONE 1.0?
None. EXAONE 1.0 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 EXAONE 1.0 open source?
No. EXAONE 1.0 has not had its weights published, so it exists only as a service controlled by its owner.
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.