DreamLLM
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
- Xi’an Jiaotong University,Megvii Inc,Tsinghua University,Huazhong University of Science and Technology
- Organisation type
- Academia,Industry,Academia,Academia
- Country
- China
- Published
- 20 September 2023
- Authors
- Runpei Dong, Chunrui Han, Yuang Peng, Zekun Qi, Zheng Ge, Jinrong Yang, Liang Zhao, Jianjian Sun, Hongyu Zhou, Haoran Wei, Xiangwen Kong, Xiangyu Zhang, Kaisheng Ma, Li Yi
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Multimodal, Language, Vision, Image generation
- Task
- Language modeling/generation, Vision-language generation, Image 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
- 7B
- Training data
- 70,369,280,000 tokens
- Epochs
- 1
7B
from Table 13: 30M pairs image and text, 4M interleaved image-text documents, and 120k instruction examples
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
- 7.5 × 10²⁰ FLOP
- How it was established
- Hardware
Table 13: 128xA800 GPU for (6+10+1.5) hours flops = (128) * ( 312 * 10**12) * (17.5 * 3600) * (0.3) = 7.5e20 (num gpu) * (peak flops) * (time in seconds) * (assumed utilization rate)
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 A800 PCIe 40 GB
- Chips used
- 128
- Chip-hours
- 2,240
- Wall-clock time
- 18 hours
- Power draw
- 63.6 kW
from Table 11: (6+10+1.5) 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
- 318
Sources
Where this record came from and when it was last checked.
- Reference
- DreamLLM: Synergistic Multimodal Comprehension and Creation
- Last updated
- 25 May 2026
What the numbers mean
About this model
DreamLLM was published by Xi’an Jiaotong University,Megvii Inc,Tsinghua University,Huazhong University of Science and Technology, in China, in September 2023. It comes out of academia,Industry,Academia,Academia.
It works in Multimodal, Language, Vision, Image generation, and is recorded as doing language modeling/generation, Vision-language generation, Image generation.
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 7.5 × 10²⁰ FLOP of arithmetic, on NVIDIA A800 PCIe 40 GB, which is a statement about the training budget rather than about inference.
Around 70,369,280,000 tokens went into training it.
Answers
DreamLLM — common questions
Is DreamLLM open source?
The licensing for DreamLLM 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 DreamLLM have?
DreamLLM has 7B parameters. 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.
Who created DreamLLM?
DreamLLM was published by Xi’an Jiaotong University,Megvii Inc,Tsinghua University,Huazhong University of Science and Technology, based in China, categorised as academia,Industry,Academia,Academia.
When was DreamLLM released?
DreamLLM was published in September 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 DreamLLM used for?
DreamLLM works in Multimodal, Language, Vision, Image generation, and is recorded as handling language modeling/generation, Vision-language generation, Image generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train DreamLLM?
Around 7.5 × 10²⁰ FLOP, on NVIDIA A800 PCIe 40 GB. 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 DreamLLM?
None. DreamLLM 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.