dnaGrinder
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
- Hong Kong Polytechnic University
- Organisation type
- Academia
- Country
- Hong Kong
- Published
- 24 September 2024
- Authors
- Qihang Zhao, Chi Zhang, Weixiong Zhang
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.
- Parameters
- 63.6M
- Training data
- 10,425,000,000 tokens
Main Pretraining: 69 billion Further Pretraining: 0.41 billion Total: 69 + 0.41 = 69.41 billion (6.94e10) tokens
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
- 2.8 × 10¹⁹ FLOP
1. Hardware setup: - Initial: 8x NVIDIA H100 SXM5 (7.56e14 FLOP/s per GPU) - Further: 1x NVIDIA A800 (7.80e13 FLOP/s) 2. Training duration (calculated from steps): - Initial: 119,000 steps × (256×2314) tokens/step = 7.04e10 tokens - Further: 31,000 steps × (32×2241) tokens/step = 2.22e9 tokens 4. Final calculation: Initial: 6 × 6.36e7 params × 7.04e10 tokens = 2.68e19 FLOPs Further: 6 × 6.36e7 params × 2.22e9 tokens = 8.46e17 FLOPs Total: 2.7712964e+19
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 H100 SXM5 80GB
- Chips used
- 8
- Power draw
- 11.0 kW
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
- dnaGrinder: a lightweight and high-capacity genomic foundation model
- Last updated
- 25 May 2026
What the numbers mean
What this model is
dnaGrinder was published by Hong Kong Polytechnic University, in Hong Kong, in September 2024. The organisation is categorised as academia.
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 2.8 × 10¹⁹ FLOP of computation, on NVIDIA H100 SXM5 80GB — a measure of what producing the model cost, not of how fast it answers.
The training set ran to roughly 10,425,000,000 tokens.
Answers
dnaGrinder — common questions
What GPU do I need to run dnaGrinder?
None. dnaGrinder 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 dnaGrinder open source?
The licensing for dnaGrinder 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 dnaGrinder have?
dnaGrinder has 63.6M parameters. 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 dnaGrinder?
dnaGrinder was published by Hong Kong Polytechnic University, based in Hong Kong, categorised as academia.
When was dnaGrinder released?
dnaGrinder was published in September 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 dnaGrinder used for?
dnaGrinder 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 dnaGrinder?
Around 2.8 × 10¹⁹ FLOP, on NVIDIA H100 SXM5 80GB. 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.
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.