Med-Gemini-M 1.5
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
- Organisation type
- Industry,Industry
- Country
- United States of America
- Published
- 1 May 2024
- Authors
- Khaled Saab, Tao Tu, Wei-Hung Weng, Ryutaro Tanno, David Stutz, Ellery Wulczyn, Fan Zhang, Tim Strother, Chunjong Park, Elahe Vedadi, Juanma Zambrano Chaves, Szu-Yeu Hu, Mike Schaekermann, Aishwarya Kamath, Yong Cheng, David G.T. Barrett, Cathy Cheung, Basil Mustafa, Anil Palepu, Daniel McDuff, Le Hou, Tomer Golany, Luyang Liu, Jean-baptiste Alayrac, Neil Houlsby, Nenad Tomasev, Jan Freyberg, Char…
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Medicine, Vision, Video, Language, Multimodal
- Task
- Medical diagnosis, Question answering, Visual question answering, Mortality prediction, Video description, Text summarization
- Base model
- Gemini 1.5 Pro
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 v4
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.
- Record confidence
- Unknown
Sources
Where this record came from and when it was last checked.
- Reference
- Capabilities of Gemini Models in Medicine
- Last updated
- 28 November 2025
What the numbers mean
What this model is
Med-Gemini-M 1.5 was published by Google DeepMind,Google Research, in United States of America, in May 2024. It comes out of industry,Industry.
It works in Medicine, Vision, Video, Language, Multimodal, and is recorded as doing medical diagnosis, Question answering, Visual question answering, Mortality prediction, Video description, Text summarization.
Its starting point was Gemini 1.5 Pro — most models at this scale are adapted from an existing base rather than built from nothing.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
Med-Gemini-M 1.5 — common questions
What GPU do I need to run Med-Gemini-M 1.5?
None. Med-Gemini-M 1.5 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 Med-Gemini-M 1.5 open source?
No. Med-Gemini-M 1.5 has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Med-Gemini-M 1.5 have?
No parameter count has been published for Med-Gemini-M 1.5, which is why no memory or speed figure appears on this page.
Who created Med-Gemini-M 1.5?
Med-Gemini-M 1.5 was published by Google DeepMind,Google Research, based in United States of America, categorised as industry,Industry.
When was Med-Gemini-M 1.5 released?
Med-Gemini-M 1.5 was published in May 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 Med-Gemini-M 1.5 used for?
Med-Gemini-M 1.5 works in Medicine, Vision, Video, Language, Multimodal, and is recorded as handling medical diagnosis, Question answering, Visual question answering, Mortality prediction, Video description, 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.