DiscDiff
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
- Imperial College London
- Organisation type
- Academia
- Country
- United Kingdom of Great Britain and Northern Ireland
- Published
- 8 February 2024
- Authors
- Zehui Li, Yuhao Ni, William A V Beardall, Guoxuan Xia, Akashaditya Das, Guy-Bart Stan, Yiren Zhao
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein or nucleotide language model (pLM/nLM)
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
- 983,040,000 tokens
160000 examples with 2048 length (Table 1) 1.6e+5*2.0e+3=3.3e+8
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
- 3.4 × 10¹⁹ FLOP
- How it was established
- Hardware
1. Hardware setup: - VAE stage: 1x NVIDIA RTX A6000 (3.87e13 FLOP/s) - UNet stage: 1x NVIDIA A100 40GB (3.12e14 FLOP/s) 2. Training duration (provided directly): - VAE: 24 GPU-hours (86,400 seconds) - UNet: 72 GPU-hours (259,200 seconds) 3. Utilization rate: 40% for both stages 4. Calculation: VAE: 3.87e13 FLOP/s × 86,400s × 0.4 = 1.34e18 FLOPs UNet: 3.12e14 FLOP/s × 259,200s × 0.4 = 3.24e19 FLOPs Total = 1.34e18 + 3.24e19 = 3.4e19 FLOPs
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 RTX A6000,NVIDIA A100
- Chips used
- 2
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
- 21
Sources
Where this record came from and when it was last checked.
- Reference
- DiscDiff: Latent Diffusion Model for DNA Sequence Generation
- Last updated
- 25 May 2026
What the numbers mean
About this model
DiscDiff was published by Imperial College London, in United Kingdom of Great Britain and Northern Ireland, in February 2024. academia is the category the publisher falls under.
It works in Biology, and is recorded as doing protein or nucleotide language model (pLM/nLM).
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
Training it took roughly 3.4 × 10¹⁹ FLOP of computation, on NVIDIA RTX A6000,NVIDIA A100 — a measure of what producing the model cost, not of how fast it answers.
It was trained on about 983,040,000 tokens of text.
Answers
DiscDiff — common questions
Is DiscDiff open source?
The licensing for DiscDiff 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 DiscDiff have?
No parameter count has been published for DiscDiff, which is why no memory or speed figure appears on this page.
Who created DiscDiff?
DiscDiff was published by Imperial College London, based in United Kingdom of Great Britain and Northern Ireland, categorised as academia.
When was DiscDiff released?
DiscDiff was published in February 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 DiscDiff used for?
DiscDiff works in Biology, and is recorded as handling protein or nucleotide language model (pLM/nLM). Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train DiscDiff?
Around 3.4 × 10¹⁹ FLOP, on NVIDIA RTX A6000,NVIDIA A100. 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 DiscDiff?
None. DiscDiff 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.