RQ-Transformer (1.4B params ImageNet dataset)
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
- Kakao,POSTECH
- Organisation type
- Industry,Academia
- Country
- Korea (Republic of)
- Published
- 3 March 2022
- Authors
- Doyup Lee, Chiheon Kim, Saehoon Kim, Minsu Cho, Wook-Shin Han
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision, Image generation
- Task
- Text-to-image
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
- 1.4B
- Training data
- 327,680,000 tokens
- Epochs
- 50
1388M from Table 5 - ImageNet rows
size of ImageNet
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
- 1.1 × 10²⁰ FLOP
- How it was established
- Hardware
"We use eight NVIDIA A100 GPUs to train RQ-Transformer of 1.4B parameters, and four GPUs to train RQ-Transformers of other sizes. The training time is <9 days for LSUN-cat, LSUN-bedroom, <4.5 days for ImageNet, and CC-3M, and <1 day for LSUN-church and FFHQ. " Quote probably involves a typo – I expect 8 A100s were used for 4.5 days to train the 3.8B model. 1.4B model probably used 4 GPUs. Best guess: (4) * (3.12e14) * (78.4 * 3600) * (0.3) = 1.057e20 (num gpu) * (peak flops) * (time in seconds…
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
- 864
- Wall-clock time
- 78 hours
- Power draw
- 6.4 kW
4.5 days for ImageNet for 1.4B model, but I expect this was a typo; probably the details are for the largest model. This also suggests training the 1.4B model used 4 rather than 8 GPUs, so would have taken ~ twice as long. Based on this, I use 4.5 days * (1.388B / 3.822B) * 2 = 78.4 hours "We use eight NVIDIA A100 GPUs to train RQ-Transformer of 1.4B parameters, and four GPUs to train RQ-Transformers of other sizes. The training time is <9 days for LSUN-cat, LSUN-bedroom, <4.5 days for ImageNe…
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
- Citations
- 763
Sources
Where this record came from and when it was last checked.
- Reference
- Autoregressive Image Generation using Residual Quantization
- Last updated
- 25 May 2026
What the numbers mean
About this model
RQ-Transformer (1.4B params ImageNet dataset) was published by Kakao,POSTECH, in Korea (Republic of), in March 2022. industry,Academia is the category the publisher falls under.
It works in Vision, Image generation, and is recorded as doing text-to-image.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
Producing it required around 1.1 × 10²⁰ FLOP of arithmetic, on NVIDIA A100, which is a statement about the training budget rather than about inference.
It was trained on about 327,680,000 tokens of text.
Answers
RQ-Transformer (1.4B params ImageNet dataset) — common questions
How much compute was used to train RQ-Transformer (1.4B params ImageNet dataset)?
Around 1.1 × 10²⁰ 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.
What GPU do I need to run RQ-Transformer (1.4B params ImageNet dataset)?
None. RQ-Transformer (1.4B params ImageNet dataset) 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 RQ-Transformer (1.4B params ImageNet dataset) open source?
The licensing for RQ-Transformer (1.4B params ImageNet dataset) 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 RQ-Transformer (1.4B params ImageNet dataset) have?
RQ-Transformer (1.4B params ImageNet dataset) has 1.4B parameters. 1388M from Table 5 - ImageNet rows. 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 RQ-Transformer (1.4B params ImageNet dataset)?
RQ-Transformer (1.4B params ImageNet dataset) was published by Kakao,POSTECH, based in Korea (Republic of), categorised as industry,Academia.
When was RQ-Transformer (1.4B params ImageNet dataset) released?
RQ-Transformer (1.4B params ImageNet dataset) was published in March 2022. 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 RQ-Transformer (1.4B params ImageNet dataset) used for?
RQ-Transformer (1.4B params ImageNet dataset) works in Vision, Image generation, and is recorded as handling text-to-image. 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?
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