Gemini Nano-1
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
- Organisation type
- Industry
- Country
- United States of America
- Published
- 19 December 2023
- Authors
- Gemini Team
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Multimodal, Language, Vision, Audio
- Task
- Chat, Image captioning, Speech recognition (ASR)
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
- 1.8B
- Training data
- tokens
1.8B
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
- Google TPU v5e
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
https://developer.android.com/ai/gemini-nano
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
- Significant use
- Record confidence
- Confident
- Citations
- 633
Significant use; deployed on Android phones such as the Pixel: https://store.google.com/intl/en/ideas/articles/pixel-feature-drop-december-2023/ "Despite their size, they show exceptionally strong performance on factuality, i.e. retrieval-related tasks, and significant performance on reasoning, STEM, coding, multimodal and multilingual tasks"
Sources
Where this record came from and when it was last checked.
- Reference
- Gemini: A Family of Highly Capable Multimodal Models
- Last updated
- 28 November 2025
What the numbers mean
About this model
Gemini Nano-1 was published by Google DeepMind, in United States of America, in December 2023. The organisation is categorised as industry.
It works in Multimodal, Language, Vision, Audio, and is recorded as doing chat, Image captioning, Speech recognition (ASR).
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
It is tracked in the underlying dataset for one reason in particular: significant use.
Answers
Gemini Nano-1 — common questions
When was Gemini Nano-1 released?
Gemini Nano-1 was published in December 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 Gemini Nano-1 used for?
Gemini Nano-1 works in Multimodal, Language, Vision, Audio, and is recorded as handling chat, Image captioning, Speech recognition (ASR). 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 Gemini Nano-1?
None. Gemini Nano-1 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 Gemini Nano-1 open source?
No. Gemini Nano-1 has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Gemini Nano-1 have?
Gemini Nano-1 has 1.8B parameters. 1.8B. 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 Gemini Nano-1?
Gemini Nano-1 was published by Google DeepMind, based in United States of America, categorised as 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.