DDIM
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
- University Paris-Saclay,Radboud University Medical Center
- Organisation type
- Academia,Academia
- Country
- France, Netherlands
- Published
- 13 April 2024
- Authors
- Alice Lacan, Romain André, Michele Sebag, Blaise Hanczar
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Gene expression profile generation
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
- 9,541,304 tokens
TCGA: 6,499 × 978 = 6,356,022 GTEx: 9,796 × 974 = 9,541,304 Total: 6,356,022 + 9,541,304 = 15,897,326 ≈ 1.6e7 datapoints
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
- 9 × 10¹⁷ FLOP
- How it was established
- Hardware
1. Hardware setup: 1x NVIDIA A40 GPU (1.50×10¹⁴ FLOP/s per GPU) 2. Training duration: 15,000 seconds (directly provided - sum of TCGA training: 3,780s and GTEx training: 11,220s) 3. Utilization rate: 40% 4. Calculation: 1.50×10¹⁴ FLOP/s × 1 GPU × 15,000s × 0.4 = 9.0×10¹⁷ FLOPs
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 4
Sources
Where this record came from and when it was last checked.
- Reference
- In Silico Generation of Gene Expression profiles using Diffusion Models
- Last updated
- 28 November 2025
What the numbers mean
Background
DDIM was published by University Paris-Saclay,Radboud University Medical Center, in France, in April 2024. academia,Academia is the category the publisher falls under.
It works in Biology, and is recorded as doing gene expression profile generation.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
Training it took roughly 9 × 10¹⁷ FLOP of computation — a measure of what producing the model cost, not of how fast it answers.
Around 9,541,304 tokens went into training it.
Answers
DDIM — common questions
Who created DDIM?
DDIM was published by University Paris-Saclay,Radboud University Medical Center, based in France, categorised as academia,Academia.
When was DDIM released?
DDIM was published in April 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 DDIM used for?
DDIM works in Biology, and is recorded as handling gene expression profile generation. These are the areas it was designed around; they describe intent rather than a hard boundary.
How much compute was used to train DDIM?
Around 9 × 10¹⁷ 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.
What GPU do I need to run DDIM?
None. DDIM 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 DDIM open source?
The licensing for DDIM was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does DDIM have?
No parameter count has been published for DDIM, which is why no memory or speed figure appears on this page.
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.