NÜWA
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
- Microsoft Research,Peking University
- Organisation type
- Industry,Academia
- Country
- United States of America, China
- Published
- 24 November 2021
- Authors
- Chenfei Wu, Jian Liang, Lei Ji, Fan Yang, Yuejian Fang, Daxin Jiang, Nan Duan
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Multimodal, Vision, Image generation, Video, Language
- Task
- Image generation, Video generation, Text-to-image, Text-to-video
- 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
- 870M
- Training data
- 5,547,780,000 tokens
Section 4.1
we first pre-train N ̈UWA on three datasets: Conceptual Captions [22] for text-to-image (T2I) generation, which includes 2.9M text-image pairs, Mo- ments in Time [26] for video prediction (V2V), which in- cludes 727K videos, and VATEX dataset [43] for text-to- video (T2V) generation, which includes 241K text-video pairs.
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
- 7.2 × 10²¹ FLOP
- How it was established
- Hardware
From AI Tracker: "Compute cost: End of Sec 4.1: "We pre-train on 64 A100 GPUs for two weeks". Half precision FLOPs of A100: 312000000000000 64 gpus *312000000000000 FLOPs *0.3 utilization * 14 day* (24*60*60) seconds / day=7.245988e+21
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
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
- Unreleased
- Training code
- Unreleased
https://github.com/microsoft/NUWA
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- SOTA improvement
- Record confidence
- Confident
- Citations
- 340
"NÜWA achieves state-of-the-art results on text-to-image generation, text-to-video generation, video prediction, etc"
Sources
Where this record came from and when it was last checked.
- Reference
- NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion
- Last updated
- 1 January 2026
What the numbers mean
Where it came from
NÜWA was published by Microsoft Research,Peking University, in the country recorded as United States of America, during November 2021. The category the publisher falls under is industry,Academia.
It works in the domain of Multimodal, Vision, Image generation, Video, Language, and is recorded as performing the task of image generation, Video generation, Text-to-image, Text-to-video.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
Producing it required arithmetic totalling around 7.2 × 10²¹ FLOP, on hardware recorded as NVIDIA A100. That figure measures what producing the model cost, and has no bearing on how fast it answers.
It was trained on a corpus of about 5,547,780,000 tokens of text.
Its inclusion criterion: sOTA improvement.
Answers
NÜWA — common questions
NÜWA— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
NÜWA— how many parameters does it have?
It has a parameter count of 870M. Section 4.1. 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.
NÜWA— who created it?
It was published by Microsoft Research,Peking University, based in United States of America, an organisation categorised as industry,Academia.
NÜWA— when was it released?
It was published in November 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.
NÜWA— what is it used for?
It works in the domain of Multimodal, Vision, Image generation, Video, Language, and is recorded as handling the task of image generation, Video generation, Text-to-image, Text-to-video. These are the areas it was designed around; they describe intent rather than a hard boundary.
NÜWA— how much compute was used to train it?
Training consumed around 7.2 × 10²¹ FLOP, on hardware recorded as 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.
NÜWA— what GPU do I need to run it?
None. This 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.