YaART
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
- Yandex
- Organisation type
- Industry
- Country
- Russia
- Published
- 8 April 2024
- Authors
- Sergey Kastryulin, Artem Konev, Alexander Shishenya, Eugene Lyapustin, Artem Khurshudov, Alexander Tselousov, Nikita Vinokurov, Denis Kuznedelev, Alexander Markovich, Grigoriy Livshits, Alexey Kirillov, Anastasiia Tabisheva, Liubov Chubarova, Marina Kaminskaia, Alexander Ustyuzhanin, Artemii Shvetsov, Daniil Shlenskii, Valerii Startsev, Dmitrii Kornilov, Mikhail Romanov, Artem Babenko, Sergei Ovch…
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Image generation
- Task
- Text-to-image, 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
- 2.3B
- Training data
- tokens
- Batch size
- 4,800
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
- 8.2 × 10¹⁹ FLOP
- How it was established
- Operation counting
There is cascade of 3 models: GEN64 Parameters: 2.3 billion Input: Textual prompt Output: 64x64 image Batch Size: 4800 Iterations: 1.1 × 10^6 Compute = 6 * 2.3 billion * 4800 * 1.1 × 10^6 = 7.2864e+19 SR256 Parameters: 700 million Input: 64x64 image Output: 256x256 image Batch Size: 960 Iterations: 1.5 × 10^6 Compute = 6 * 700 million * 960 * 1.5 × 10^6 = 6.048e+18 SR1024 Parameters: 700 million Input: 256x256 image Output: 1024x1024 image Batch Size: 512 Iterations: 1.5 × 10^6 Compute …
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 SXM4 80 GB
- Chips used
- 160
- Power draw
- 126.5 kW
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
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Speculative
Sources
Where this record came from and when it was last checked.
- Reference
- YaART: Yet Another ART Rendering Technology
- Last updated
- 28 November 2025
What the numbers mean
What this model is
YaART was published by Yandex, in the country recorded as Russia, during April 2024. It comes out of an organisation categorised as industry.
It works in the domain of Image generation, and is recorded as performing the task of text-to-image, Image generation.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
Training it took a computation budget of roughly 8.2 × 10¹⁹ FLOP, on hardware recorded as NVIDIA A100 SXM4 80 GB. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
YaART — common questions
YaART— 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.
YaART— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
YaART— how many parameters does it have?
It has a parameter count of 2.3B. 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.
YaART— who created it?
It was published by Yandex, based in Russia, an organisation categorised as industry.
YaART— when was it released?
It was published in April 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
YaART— what is it used for?
It works in the domain of Image generation, and is recorded as handling the task of text-to-image, Image generation. These are the areas it was designed around; they describe intent rather than a hard boundary.
YaART— how much compute was used to train it?
Training consumed around 8.2 × 10¹⁹ FLOP, on hardware recorded as NVIDIA A100 SXM4 80 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.
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.