EXAONE 3.5-R 32B
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
- 14 March 2025
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation, Question answering, Translation
- Base model
- EXAONE 3.5 32B
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
- 32B
- Training data
- tokens
32B
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
- 1.3 × 10²⁴ FLOP
- How it was established
- Reported
- Fine-tuning compute
- 1.9 × 10²² FLOP
1.25 × 10^24 (base model reported training compute) + 1.92 × 10^22 (finetune compute) = 1.2692e+24 FLOP
1.92e22
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.
- Likely above 10²³ FLOP
- Yes
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Last updated
- 28 November 2025
What the numbers mean
Background
EXAONE 3.5-R 32B was published by LG AI Research, in Korea (Republic of), in March 2025. The organisation is categorised as industry.
It works in Language, and is recorded as doing language modeling/generation, Question answering, Translation.
It builds on EXAONE 3.5 32B, which is why it shares that model's general shape and size.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
Producing it required around 1.3 × 10²⁴ FLOP of arithmetic, which is a statement about the training budget rather than about inference.
Answers
EXAONE 3.5-R 32B — common questions
How much compute was used to train EXAONE 3.5-R 32B?
Around 1.3 × 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 3.5-R 32B?
None. EXAONE 3.5-R 32B 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 3.5-R 32B open source?
No. EXAONE 3.5-R 32B has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does EXAONE 3.5-R 32B have?
EXAONE 3.5-R 32B has 32B parameters. 32B. 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 3.5-R 32B?
EXAONE 3.5-R 32B was published by LG AI Research, based in Korea (Republic of), categorised as industry.
When was EXAONE 3.5-R 32B released?
EXAONE 3.5-R 32B was published in March 2025.
What is EXAONE 3.5-R 32B used for?
EXAONE 3.5-R 32B works in Language, and is recorded as handling language modeling/generation, Question answering, Translation. These are the areas it was designed around; they describe intent rather than a hard boundary.
Source
Record last updated 28 November 2025
The other direction
Looking at it from the other side?
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