Gemini 2.0 Flash
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
- Organisation type
- Industry,Industry
- Country
- United States of America
- Published
- 11 December 2024
- Authors
- Gemini Team
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language, Vision, Audio, Speech, Video, Multimodal
- Task
- Language modeling/generation, Question answering, Visual question answering, Speech recognition (ASR), Code generation, Quantitative reasoning, Video description, Translation, Chat, Table tasks, Search, Text summarization
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
- Google TPU v6e Trillium
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
Availability Google AI Studio Gemini API Vertex AI Gemini App
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Likely above 10²³ FLOP
- Yes
- Record confidence
- Unknown
Sources
Where this record came from and when it was last checked.
- Reference
- Introducing Gemini 2.0: our new AI model for the agentic era
- Last updated
- 11 February 2026
What the numbers mean
Background
Gemini 2.0 Flash was published by Google DeepMind,Google, in United States of America, in December 2024. It comes out of industry,Industry.
It works in Language, Vision, Audio, Speech, Video, Multimodal, and is recorded as doing language modeling/generation, Question answering, Visual question answering, Speech recognition (ASR), Code generation, Quantitative reasoning, Video description, Translation, Chat, Table tasks, Search, Text summarization.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
Gemini 2.0 Flash — common questions
What GPU do I need to run Gemini 2.0 Flash?
None. Gemini 2.0 Flash 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 2.0 Flash open source?
No. Gemini 2.0 Flash has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Gemini 2.0 Flash have?
No parameter count has been published for Gemini 2.0 Flash, which is why no memory or speed figure appears on this page.
Who created Gemini 2.0 Flash?
Gemini 2.0 Flash was published by Google DeepMind,Google, based in United States of America, categorised as industry,Industry.
When was Gemini 2.0 Flash released?
Gemini 2.0 Flash 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 Gemini 2.0 Flash used for?
Gemini 2.0 Flash works in Language, Vision, Audio, Speech, Video, Multimodal, and is recorded as handling language modeling/generation, Question answering, Visual question answering, Speech recognition (ASR), Code generation, Quantitative reasoning, Video description, Translation, Chat, Table tasks, Search, Text summarization. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
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.