OtterHD-8B
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
- Nanyang Technological University
- Organisation type
- Academia
- Country
- Singapore
- Published
- 7 November 2023
- Authors
- Bo Li, Peiyuan Zhang, Jingkang Yang, Yuanhan Zhang, Fanyi Pu, Ziwei Liu
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Multimodal, Vision, Language
- Task
- Chat, Visual question answering
- Base model
- Fuyu-8B
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
- 8B
- Training data
- 200,540,000 tokens
- Epochs
- 3
8B
"We compiled a total of 370K instruction/response pairs sourced from the follow- ing public datasets: LLaVA-Instruct [ 30], VQAv2 [2], GQA [ 23 ], OKVQA [ 36 ], OCRVQA [38 ], A-OKVQA [ 45], COCO-GOI [33 ], COCO-Caption [ 10], TextQA [ 48], RefCOCO [58], COCO-ITM [ 28 ], ImageNet [17 ], and LLaVA-RLHF [ 51 ]."
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.
- Fine-tuning compute
- 2.4 × 10¹⁹ FLOP
flops = (8) * (312 * 10**12) * (3 * 3 * 3600) * (0.3) = 2.4e19 (num gpu) * (peak flops) * (time in seconds) * (assumed utilization rate) 'Our implementation permits the completion of full-parameter training within 3 hours per epoch on 8×A100 GPUs. ' Table 4 indicates 3 epochs for the full finetune.
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
- 8
- Chip-hours
- 24
- Wall-clock time
- 9 hours
- Power draw
- 6.3 kW
3 hours per epoch over 3 epochs: 9 hours
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 85
Sources
Where this record came from and when it was last checked.
- Reference
- OtterHD: A High-Resolution Multi-modality Model
- Last updated
- 25 May 2026
What the numbers mean
Background
OtterHD-8B was published by Nanyang Technological University, in Singapore, in November 2023. The organisation is categorised as academia.
It works in Multimodal, Vision, Language, and is recorded as doing chat, Visual question answering.
Its starting point was Fuyu-8B — most models at this scale are adapted from an existing base rather than built from nothing.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
It was trained on about 200,540,000 tokens of text.
Answers
OtterHD-8B — common questions
Is OtterHD-8B open source?
The licensing for OtterHD-8B was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does OtterHD-8B have?
OtterHD-8B has 8B parameters. 8B. 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 OtterHD-8B?
OtterHD-8B was published by Nanyang Technological University, based in Singapore, categorised as academia.
When was OtterHD-8B released?
OtterHD-8B was published in November 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 OtterHD-8B used for?
OtterHD-8B works in Multimodal, Vision, Language, and is recorded as handling chat, Visual question answering. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run OtterHD-8B?
None. OtterHD-8B 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.
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.