K-EXAONE
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 AI Research
- Organisation type
- Industry
- Country
- Korea (Republic of)
- Published
- 31 December 2025
- Authors
- Eunbi Choi, Kibong Choi, Seokhee Hong, Junwon Hwang, Hyojin Jeon, Hyunjik Jo, Joonkee Kim, Seonghwan Kim, Soyeon Kim, Sunkyoung Kim, Yireun Kim, Yongil Kim, Haeju Lee, Jinsik Lee, Kyungmin Lee, Sangha Park, Heuiyeen Yeen
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation, Chat, Question answering, Code generation, Mathematical reasoning, Instruction interpretation
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
- 236B
- Training data
- 11,000,000,000,000 tokens
- Epochs
- 1
236B
11T 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
- 1.5 × 10²⁴ FLOP
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA B200 GPUs
- Chips used
- 512
- Wall-clock time
- 3,240 hours (135 days)
K-EXAONE was trained using a cloud-based GPU environment. The total training duration was 135 days, utilizing 512 B200 GPUs. This corresponds to a total of 1,658,880 GPU hours, calculated as follows: 512 GPUs × 135 days × 24 hours = 1,658,880 GPU hours
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
- API access
- Training code
- Unreleased
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
- Training cost
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- K-EXAONE Technical Report
- Last updated
- 13 January 2026
What the numbers mean
Background
K-EXAONE was published by LG AI Research, in the country recorded as Korea (Republic of), during December 2025. The publishing organisation is categorised as industry.
It works in the domain of Language, and is recorded as performing the task of language modeling/generation, Chat, Question answering, Code generation, Mathematical reasoning, Instruction interpretation.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
Training it took a computation budget of roughly 1.5 × 10²⁴ FLOP, on hardware recorded as NVIDIA B200 GPUs. That figure measures what producing the model cost, and has no bearing on how fast it answers.
It was trained on a corpus of about 11,000,000,000,000 tokens of text.
Its inclusion criterion: training cost.
Answers
K-EXAONE — common questions
K-EXAONE— what GPU do I need to run it?
None. This 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.
K-EXAONE— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
K-EXAONE— how many parameters does it have?
It has a parameter count of 236B. 236B. 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.
K-EXAONE— who created it?
It was published by LG AI Research, based in Korea (Republic of), an organisation categorised as industry.
K-EXAONE— when was it released?
It was published in December 2025.
K-EXAONE— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling/generation, Chat, Question answering, Code generation, Mathematical reasoning, Instruction interpretation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
K-EXAONE— how much compute was used to train it?
Training consumed around 1.5 × 10²⁴ FLOP, on hardware recorded as NVIDIA B200 GPUs. 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.