Amazon Nova Lite
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
- Amazon
- Organisation type
- Industry
- Country
- United States of America
- Published
- 3 December 2024
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Multimodal, Language, Video, Vision
- Task
- Language modeling/generation, Retrieval-augmented generation, Visual question answering, Image captioning, Video description, Character recognition (OCR), Code generation, Translation
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.
- Training data
- tokens
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
- Amazon Trainium1,NVIDIA A100,NVIDIA H100 SXM5 80GB
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
- Unknown
Sources
Where this record came from and when it was last checked.
- Reference
- Introducing Amazon Nova foundation models: Frontier intelligence and industry leading price performance
- Last updated
- 28 November 2025
What the numbers mean
Background
Amazon Nova Lite was published by Amazon, in United States of America, in December 2024. The organisation is categorised as industry.
It works in Multimodal, Language, Video, Vision, and is recorded as doing language modeling/generation, Retrieval-augmented generation, Visual question answering, Image captioning, Video description, Character recognition (OCR), Code generation, Translation.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
Amazon Nova Lite — common questions
Is Amazon Nova Lite open source?
No. Amazon Nova Lite has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Amazon Nova Lite have?
No parameter count has been published for Amazon Nova Lite, which is why no memory or speed figure appears on this page.
Who created Amazon Nova Lite?
Amazon Nova Lite was published by Amazon, based in United States of America, categorised as industry.
When was Amazon Nova Lite released?
Amazon Nova Lite was published in December 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.
What is Amazon Nova Lite used for?
Amazon Nova Lite works in Multimodal, Language, Video, Vision, and is recorded as handling language modeling/generation, Retrieval-augmented generation, Visual question answering, Image captioning, Video description, Character recognition (OCR), Code generation, Translation. 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 Amazon Nova Lite?
None. Amazon Nova Lite 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.