Mi:dm 200B
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
- KT
- Organisation type
- Industry
- Country
- Korea (Republic of)
- Published
- 31 October 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
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
- 200B
- Training data
- 1,000,000,000,000 tokens
200B
Mi:dm is the first Korean LLM trained on over 1 trillion 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
- 1.2 × 10²⁴ FLOP
- How it was established
- Operation counting
6ND=1000000000000*200000000000.00*6=1.2 × 10^24
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
KT said it will open up the foundation model of Mi:dm to other companies, providing a full AI development package, including KT Cloud's hyperscale AI computing service and AI chip startup Rebellions Inc.'s neural processing unit infrastructure, fostering the development of various AI services.
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
Mi:dm 200B was published by KT, in Korea (Republic of), in October 2023. It comes out of industry.
It works in Language, and is recorded as doing language modeling/generation.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
Training it took roughly 1.2 × 10²⁴ FLOP of computation — a measure of what producing the model cost, not of how fast it answers.
It was trained on about 1,000,000,000,000 tokens of text.
Answers
Mi:dm 200B — common questions
How much compute was used to train Mi:dm 200B?
Around 1.2 × 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 Mi:dm 200B?
None. Mi:dm 200B 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 Mi:dm 200B open source?
No. Mi:dm 200B has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Mi:dm 200B have?
Mi:dm 200B has 200B parameters. 200B. 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 Mi:dm 200B?
Mi:dm 200B was published by KT, based in Korea (Republic of), categorised as industry.
When was Mi:dm 200B released?
Mi:dm 200B was published in October 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.
What is Mi:dm 200B used for?
Mi:dm 200B works in Language, and is recorded as handling language modeling/generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
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.