Uni-Med
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
- Tsinghua University,Beijing University of Posts and Telecommunications
- Organisation type
- Academia,Academia
- Country
- China
- Published
- 1 November 2024
- Authors
- Xun Zhu, Ying Hu, Fanbin Mo, Miao Li, Ji Wu
What it does
The problem areas the model was built for. A model can carry several of each.
- Base model
- Llama 2-7B,ViT-G/14
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
- 8.8B
- Training data
- tokens
7B (LLama) + 1843000000 (ViT) = 8.8B
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
- 1.4 × 10²³ FLOP
- Fine-tuning compute
- 5.6 × 10¹⁸ FLOP
Finetune: 10*60*60*312000000000000*0.5=5.616e+18 Base models: 8.4e+22, 5.85e+22 Total: 1.4250562e+23
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
- NVIDIA A800 SXM
- Chips used
- 1
- Wall-clock time
- 10 hours
- Power draw
- 433 W
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
- Open (restricted use)
https://github.com/MSIIP/Uni-Med (undefined license) I haven't found released pre-trained weights
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
- Citations
- 23
Sources
Where this record came from and when it was last checked.
- Reference
- Uni-Med: A Unified Medical Generalist Foundation Model For Multi-Task Learning Via Connector-MoE
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
Uni-Med was published by Tsinghua University,Beijing University of Posts and Telecommunications, in China, in November 2024. academia,Academia is the category the publisher falls under.
Its starting point was Llama 2-7B,ViT-G/14 — 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.
Training and provenance
Training it took roughly 1.4 × 10²³ FLOP of computation, on NVIDIA A800 SXM — a measure of what producing the model cost, not of how fast it answers.
Answers
Uni-Med — common questions
How many parameters does Uni-Med have?
Uni-Med has 8.8B parameters. 7B (LLama) + 1843000000 (ViT) = 8.8B. 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 Uni-Med?
Uni-Med was published by Tsinghua University,Beijing University of Posts and Telecommunications, based in China, categorised as academia,Academia.
When was Uni-Med released?
Uni-Med was published in November 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.
How much compute was used to train Uni-Med?
Around 1.4 × 10²³ FLOP, on NVIDIA A800 SXM. 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.
What GPU do I need to run Uni-Med?
None. Uni-Med 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 Uni-Med open source?
No. Uni-Med has not had its weights published, so it exists only as a service controlled by its owner.
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.