HyperCLOVA 82B
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
- NAVER,Search Solutions
- Organisation type
- Industry,Industry
- Country
- Korea (Republic of)
- Published
- 10 September 2021
- Authors
- Boseop Kim, HyoungSeok Kim, Sang-Woo Lee, Gichang Lee, Donghyun Kwak, Dong Hyeon Jeon, Sunghyun Park, Sungju Kim, Seonhoon Kim, Dongpil Seo, Heungsub Lee, Minyoung Jeong, Sungjae Lee, Minsub Kim, Suk Hyun Ko, Seokhun Kim, Taeyong Park, Jinuk Kim, Soyoung Kang, Na-Hyeon Ryu, Kang Min Yoo, Minsuk Chang, Soobin Suh, Sookyo In, Jinseong Park, Kyungduk Kim, Hiun Kim, Jisu Jeong, Yong Goo Yeo, Donghoon …
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, Translation, Text classification
- Approach
- Self-supervised learning
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
- 82B
- Training data
- 300,000,000,000 tokens
"We introduce a Korean in-context large-scale LM with 82B parameters, i.e., HyperCLOVA. This is the first discovery on near 100B-scale non-English LM." According to media reports, HyperCLOVA has 204B parameters (i.e. a different version than in the paper) https://m.koreaherald.com/view.php?ud=20210525000824
"However, experiments in Section 5.2 use the model trained with 300B tokens, as HyperCLOVA Studio provided the 39B and 82B models trained with 300B tokens." "We introduce HyperCLOVA, a large-scale Korean in-context learning-based LM with nearly 100B parameters, by constructing a large Korean-centric corpus of 560B tokens." Based on tokenizing the Hyperclova article itself using OpenAI's tiktoken BPE tokenizer (https://github.com/openai/tiktoken), there are 3285 tokens for 1069 words - about 3 …
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 × 10²³ FLOP
- How it was established
- Operation counting,Hardware
"For experiments in Section 4, the model trained with 150B is used for fair comparison, because not all models are finished training at the same iteration. However, experiments in Section 5.2 use the model trained with 300B tokens, as HyperCLOVA Studio provided the 39B and 82B models trained with 300B tokens." 82e9 connections * 2 FLOP/connection * 300e9 tokens * 3 backward pass = 1.476e23 FLOP Calculation using GPU time corroborates this: - "Our model is based on megatron-LM (Shoeybi et al., …
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 A100
- Chips used
- 1,024
- Chip-hours
- 658,637
- Wall-clock time
- 643 hours (26.8 days)
- Hardware utilisation
- MFU 20.0%
- Power draw
- 827.0 kW
- Compute cost
- $586,409
see compute notes
6ND method gives 1.476e23 FLOP needed to train the model. Actual use was 1,024 A100 GPUs for 13.4 days for 150B tokens; 300B model probably took twice as long. 26.8 * 24 * 3600 * 1024 * 3.12e14 = 7.3978e23 1.476e23 / 7.3978e23 MFU = 0.1995
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
"We introduce HyperCLOVA Studio, an interactive prompt engineering interface which provides GUI and API interfaces like the OpenAI playground1"
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Foundation model
- Yes
- Likely above 10²³ FLOP
- Yes
- Why it is tracked
- SOTA improvement
- Record confidence
- Confident
- Citations
- 131
"HyperCLOVA with our training configuration shows state-of-the-art in-context zero-shot and few-shot learning performances on various downstream tasks in Korean" I don't see any standard benchmark that they claim SOTA on
Sources
Where this record came from and when it was last checked.
- Reference
- What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers
- Last updated
- 25 May 2026
What the numbers mean
What this model is
HyperCLOVA 82B was published by NAVER,Search Solutions, in Korea (Republic of), in September 2021. The organisation is categorised as industry,Industry.
It works in Language, and is recorded as doing language modeling/generation, Chat, Translation, Text classification.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
The training run consumed about 1.5 × 10²³ FLOP, on NVIDIA A100. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
The training set ran to roughly 300,000,000,000 tokens.
Its inclusion criterion is sOTA improvement.
Answers
HyperCLOVA 82B — common questions
What GPU do I need to run HyperCLOVA 82B?
None. HyperCLOVA 82B 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 HyperCLOVA 82B open source?
No. HyperCLOVA 82B has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does HyperCLOVA 82B have?
HyperCLOVA 82B has 82B parameters. "We introduce a Korean in-context large-scale LM with 82B parameters, i.e., HyperCLOVA. This is the first discovery on near 100B-scale non-English LM." According to media reports, HyperCLOVA has 204B parameters (i.e. a different version than in the paper) https://m.koreaherald.com/view.php?ud=20210525000824. 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 HyperCLOVA 82B?
HyperCLOVA 82B was published by NAVER,Search Solutions, based in Korea (Republic of), categorised as industry,Industry.
When was HyperCLOVA 82B released?
HyperCLOVA 82B was published in September 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 HyperCLOVA 82B used for?
HyperCLOVA 82B works in Language, and is recorded as handling language modeling/generation, Chat, Translation, Text classification. These are the areas it was designed around; they describe intent rather than a hard boundary.
How much compute was used to train HyperCLOVA 82B?
Around 1.5 × 10²³ FLOP, on NVIDIA A100. 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.