PaLI-3
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
- Google DeepMind,Google Research,Google Cloud
- Organisation type
- Industry,Industry,Industry
- Country
- United States of America
- Published
- 17 October 2023
- Authors
- Xi Chen, Xiao Wang, Lucas Beyer, Alexander Kolesnikov, Jialin Wu, Paul Voigtlaender, Basil Mustafa, Sebastian Goodman, Ibrahim Alabdulmohsin, Piotr Padlewski, Daniel Salz, Xi Xiong, Daniel Vlasic, Filip Pavetic, Keran Rong, Tianli Yu, Daniel Keysers, Xiaohua Zhai, Radu Soricut
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Multimodal, Language, Vision
- Task
- Visual question answering, Character recognition (OCR), Image captioning
- Base model
- UL2
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
- 5B
- Training data
- tokens
5B
contrastive image pretraining at 224² then high‑res 812–1064²
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
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
Table 2, Table 5
Sources
Where this record came from and when it was last checked.
- Reference
- PaLI-3 Vision Language Models: Smaller, Faster, Stronger
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
PaLI-3 was published by Google DeepMind,Google Research,Google Cloud, in United States of America, in October 2023. The organisation is categorised as industry,Industry,Industry.
It works in Multimodal, Language, Vision, and is recorded as doing visual question answering, Character recognition (OCR), Image captioning.
It is derived from UL2 rather than trained from scratch, which is the usual way a specialised model is produced.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
PaLI-3 — common questions
When was PaLI-3 released?
PaLI-3 was published in October 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 PaLI-3 used for?
PaLI-3 works in Multimodal, Language, Vision, and is recorded as handling visual question answering, Character recognition (OCR), Image captioning. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
What GPU do I need to run PaLI-3?
None. PaLI-3 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 PaLI-3 open source?
No. PaLI-3 has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does PaLI-3 have?
PaLI-3 has 5B parameters. 5B. 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 PaLI-3?
PaLI-3 was published by Google DeepMind,Google Research,Google Cloud, based in United States of America, categorised as industry,Industry,Industry.
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.