VARCO LLM 2.0 small Finetuning
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
- NCSOFT
- Organisation type
- Industry
- Country
- Korea (Republic of)
- Published
- 16 August 2023
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, Text summarization, Translation, 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
- 7B
- Training data
- 1,600,000,000,000 tokens
https://ncsoft.github.io/ncresearch/varco-llm-details/
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
- 6.7 × 10²² FLOP
- How it was established
- Operation counting
=1600000000000*6*7000000000=6.72 × 10^22
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
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
Sources
Where this record came from and when it was last checked.
- Reference
- VARCO LLM 2.0 is NCSOFT's large language model that can be applied to the development of natural language processing-based AI services.
- Last updated
- 28 November 2025
What the numbers mean
What this model is
VARCO LLM 2.0 small Finetuning was published by NCSOFT, in the country recorded as Korea (Republic of), during August 2023. 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, Text summarization, Translation, Question answering.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
How it was trained
Training it took a computation budget of roughly 6.7 × 10²² FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Training consumed a corpus of around 1,600,000,000,000 tokens of text.
Answers
VARCO LLM 2.0 small Finetuning — common questions
VARCO LLM 2.0 small Finetuning— 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.
VARCO LLM 2.0 small Finetuning— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
VARCO LLM 2.0 small Finetuning— how many parameters does it have?
It has a parameter count of 7B. 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.
VARCO LLM 2.0 small Finetuning— who created it?
It was published by NCSOFT, based in Korea (Republic of), an organisation categorised as industry.
VARCO LLM 2.0 small Finetuning— when was it released?
It was published in August 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.
VARCO LLM 2.0 small Finetuning— what is it used for?
It works in the domain of Language, and is recorded as handling the task of language modeling/generation, Chat, Text summarization, Translation, Question answering. 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.
VARCO LLM 2.0 small Finetuning— how much compute was used to train it?
Training consumed around 6.7 × 10²² 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.