AMIE (Articulate Medical Intelligence Explorer)
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
- 9 April 2025
- Authors
- Tao Tu, Mike Schaekermann, Anil Palepu, Khaled Saab, Jan Freyberg, Ryutaro Tanno, Amy Wang, Brenna Li, Mohamed Amin, Yong Cheng, Elahe Vedadi, Nenad Tomasev, Shekoofeh Azizi, Karan Singhal, Le Hou, Albert Webson, Kavita Kulkarni, S. Sara Mahdavi, Christopher Semturs, Juraj Gottweis, Joelle Barral, Katherine Chou, Greg S. Corrado, Yossi Matias, Alan Karthikesalingam, Vivek Natarajan
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Medicine, Language
- Task
- Medical diagnosis, Language modeling/generation, Question answering, Chat
- Base model
- PaLM 2
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
- 340B
- Training data
- tokens
Supposedly same amount of parameters as Palm 2 Large
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 7.3 × 10²⁴ FLOP
- How it was established
- Comparison with other models
Assuming negligible fine-tune compute base model (Palm 2): 7.34e+24 FLOP
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
"AMIE is an LLM-based research AI system for diagnostic dialogue. We are not making the model code and weights open source owing to the safety implications of unmonitored use of such a system in medical settings. In the interest of responsible innovation, we will be working with research partners, regulators and providers to validate and explore safe onward uses of AMIE. For reproducibility, we have documented technical deep learning methods while keeping the paper accessible to a clinical and g…
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
- Likely
Sources
Where this record came from and when it was last checked.
- Reference
- Towards conversational diagnostic artificial intelligence
- Last updated
- 11 February 2026
What the numbers mean
Where it came from
AMIE (Articulate Medical Intelligence Explorer) was published by Google DeepMind,Google Research, in the country recorded as United States of America, during April 2025. The category the publisher falls under is industry,Industry.
It works in the domain of Medicine, Language, and is recorded as performing the task of medical diagnosis, Language modeling/generation, Question answering, Chat.
It builds on PaLM 2. Most models at this scale are adapted from an existing base rather than built from nothing.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
The training run consumed about 7.3 × 10²⁴ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
AMIE (Articulate Medical Intelligence Explorer) — common questions
AMIE (Articulate Medical Intelligence Explorer)— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
AMIE (Articulate Medical Intelligence Explorer)— how many parameters does it have?
It has a parameter count of 340B. Supposedly same amount of parameters as Palm 2 Large. 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.
AMIE (Articulate Medical Intelligence Explorer)— who created it?
It was published by Google DeepMind,Google Research, based in United States of America, an organisation categorised as industry,Industry.
AMIE (Articulate Medical Intelligence Explorer)— when was it released?
It was published in April 2025.
AMIE (Articulate Medical Intelligence Explorer)— what is it used for?
It works in the domain of Medicine, Language, and is recorded as handling the task of medical diagnosis, Language modeling/generation, Question answering, Chat. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
AMIE (Articulate Medical Intelligence Explorer)— how much compute was used to train it?
Training consumed around 7.3 × 10²⁴ FLOP. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
AMIE (Articulate Medical Intelligence Explorer)— what GPU do I need to run it?
None. This 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.