Nucleotide Transformer TPS calculator

Open weights NVIDIA,Technical University of Munich,InstaDeep 2.5B parameters January 2023

Each card below is assessed against this model at the context length and minimum quality you choose. Speed is an estimate for a single request, calculated from the card's memory bandwidth and the size of the model once compressed.

Calculated for this model

818 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Tesla C1080

4 GB · Q8_0 · 14.8 tok/s

Fastest card

B200

1,355 tok/s · 180 GB

Which GPUs can run Nucleotide Transformer?

Set the inputs, read the answer

Context length tokens

A longer conversation needs more memory, which can push this model off smaller cards.

Minimum quality

Hides cards that would only fit the model by compressing it below this point.

Search cards

Card memory

Also show cards that cannot run it

818 cards match

Calculating

					Needs	Quantisation	Fit
1,355 tok/s 813–2,168 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	3.4 GB	Q8_0	Comfortable
1,355 tok/s 813–2,168 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	3.4 GB	Q8_0	Comfortable
1,082 tok/s 649–1,732 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	3.4 GB	Q8_0	Comfortable
1,082 tok/s 649–1,732 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	3.4 GB	Q8_0	Comfortable
866 tok/s 519–1,385 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	3.4 GB	Q8_0	Comfortable
828 tok/s 497–1,325 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	3.4 GB	Q8_0	Comfortable
828 tok/s 497–1,325 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	3.4 GB	Q8_0	Comfortable
793 tok/s 476–1,269 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	3.4 GB	Q8_0	Comfortable
704 tok/s 422–1,126 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	3.4 GB	Q8_0	Comfortable
704 tok/s 422–1,126 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	3.4 GB	Q8_0	Comfortable
704 tok/s 422–1,126 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	3.4 GB	Q8_0	Comfortable
667 tok/s 400–1,068 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	3.4 GB	Q8_0	Comfortable
569 tok/s 342–911 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	3.4 GB	Q8_0	Comfortable
569 tok/s 342–911 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	3.4 GB	Q8_0	Comfortable
569 tok/s 342–911 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	3.4 GB	Q8_0	Comfortable
569 tok/s 342–911 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	3.4 GB	Q8_0	Comfortable
569 tok/s 342–911 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	3.4 GB	Q8_0	Comfortable
433 tok/s 260–693 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	3.4 GB	Q8_0	Comfortable
433 tok/s 260–693 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	3.4 GB	Q8_0	Comfortable
361 tok/s 217–578 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	3.4 GB	Q8_0	Comfortable
353 tok/s 212–566 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	3.4 GB	Q8_0	Comfortable
346 tok/s 207–553 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	3.4 GB	Q8_0	Comfortable
346 tok/s 207–553 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	3.4 GB	Q8_0	Comfortable
346 tok/s 207–553 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	3.4 GB	Q8_0	Comfortable
346 tok/s 207–553 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	3.4 GB	Q8_0	Comfortable

Speeds are estimates for a single request — one conversation at a time — calculated from memory bandwidth, model size and quantisation. Real throughput varies with the inference runtime and its version. Figures published by hardware vendors measure many simultaneous requests and are much higher.

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: NVIDIA,Technical University of Munich,InstaDeep
Organisation type: Industry,Academia,Industry
Country: United States of America, Germany, United Kingdom of Great Britain and Northern Ireland
Published: 15 January 2023
Authors: Hugo Dalla-Torre, Liam Gonzalez, Javier Mendoza Revilla, Nicolas Lopez Carranza, Adam Henryk Grzywaczewski, Francesco Oteri, Christian Dallago, Evan Trop, Hassan Sirelkhatim, Guillaume Richard, Marcin Skwark, Karim Beguir, Marie Lopez, Thomas Pierrot

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), Nucleotide generation
Approach: Self-supervised learning
Numerical format: FP32

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: 2.5B
Training data: 300,000,000,000 tokens
Epochs: 1

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: 8.1 × 10²¹ FLOP
How it was established: Operation counting,Hardware
Fine-tuning compute: 9.4 × 10¹⁷ FLOP

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 A100
Chips used: 128
Chip-hours: 86,016
Wall-clock time: 672 hours (28 days)
Power draw: 102.2 kW
Compute cost: $51,065

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: Open — downloadable
Model access: Open weights (non-commercial)
Training code: Unreleased

How it is classified

Labels the source dataset applies when tracking notable models, and how confident it is in the entry.

Why it is tracked: SOTA improvement
Record confidence: Likely
Citations: 22

Sources

Where this record came from and when it was last checked.

Reference: The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Nucleotide Transformer

Ranked by estimated tokens per second, newest card first where speeds tie. Because generation is bound by memory bandwidth, this ordering follows bandwidth rather than any gaming benchmark.

The smallest GPUs that still run Nucleotide Transformer

The cheapest route in, by memory capacity. A tight fit runs the model but leaves nothing spare for a longer conversation.

What the numbers mean

The hardware side

Minimum card

Tesla C1080

Memory needed

3.4 GB

Fastest

1,355 tok/s

Nucleotide Transformer is small enough at 2.5B parameters that hardware is rarely the obstacle — 818 of the cards we track can run it, including cards several years old.

At the low end, a Tesla C1080 handles it — 4 GB, at Q8_0, for about 14.8 tokens per second.

A B200 is the fastest we calculate for it: about 1,355 tokens per second, from 8,000 GB/s of memory bandwidth.

Where it came from

Nucleotide Transformer was published by NVIDIA,Technical University of Munich,InstaDeep, in United States of America, in January 2023. industry,Academia,Industry is the category the publisher falls under.

It works in Biology, and is recorded as doing protein or nucleotide language model (pLM/nLM), Nucleotide generation.

The weights being open is what puts this page in the calculator rather than only in the catalogue: it is a model you can actually hold.

Understanding the speeds

The median result is around 38.1 tokens per second; 783 cards produce text faster than most people read it.

Being dense, it reads all of itself per token, which is why the ordering by speed below follows the ordering by memory bandwidth so closely.

Memory here is estimated from size rather than computed from the architecture, which is not recorded for this model — the numbers are indicative rather than exact.

Training and provenance

Producing it required around 8.1 × 10²¹ FLOP of arithmetic, on NVIDIA A100, which is a statement about the training budget rather than about inference.

Around 300,000,000,000 tokens went into training it.

The reason it appears in this catalogue at all is sOTA improvement.

Step by step

How to choose a GPU for Nucleotide Transformer

The table above has already assessed every card we hold specifications for against this model. Getting to your answer takes six steps.

01

Start from the memory column

The table lists every card that can hold Nucleotide Transformer — around 3.4 GB at Q8_0. That figure, not the card's headline performance, is what decides whether it runs.
02

Set the context length you will work at

The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context Nucleotide Transformer can slip off a card that handles short questions easily.
03

Decide how much compression you will accept

Compression is what makes Nucleotide Transformer fit smaller cards, at some cost in accuracy — Q8_0 on the smallest card that fits. A minimum quality removes the ones that go too far.
04

Sort by speed

Sort by speed to see how cards rank for Nucleotide Transformer. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 1,355 tok/s.
05

Look at the headroom, not just the fit

Tight means Nucleotide Transformer loads and works, with no room to raise the context later. Comfortable means you can. The difference matters more than a few tokens per second.
06

Check the card from the other side

Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond Nucleotide Transformer.

Answers

Nucleotide Transformer — common questions

When was Nucleotide Transformer released?

Nucleotide Transformer was published in January 2023. 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 Nucleotide Transformer used for?

Nucleotide Transformer works in Biology, and is recorded as handling protein or nucleotide language model (pLM/nLM), Nucleotide generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Where can I download Nucleotide Transformer?

The weights for Nucleotide Transformer are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.

How much compute was used to train Nucleotide Transformer?

Around 8.1 × 10²¹ FLOP, on 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.

Can I run Nucleotide Transformer if it does not fit in my GPU?

Only by offloading, which is usually a false economy: the part in system memory drags the whole thing down. Our figures for Nucleotide Transformer assume it is fully resident.

Would two GPUs run Nucleotide Transformer faster?

Capacity adds across cards; throughput does not. Since 818 of the cards we track already hold Nucleotide Transformer on their own, a second card is rarely the answer here.

Why does the quantisation differ between cards for Nucleotide Transformer?

A larger card holds a more accurate copy. Across the cards that run Nucleotide Transformer, 1 compression levels are used; the floor control above pins it to one.

How accurate are these Nucleotide Transformer speed estimates?

These are estimates with real error bars. The fastest result here, 813–2,168 tok/s on the B200, could reasonably land anywhere in its published range depending on which runtime you use.

What GPU do I need to run Nucleotide Transformer?

The smallest card in our catalogue that holds Nucleotide Transformer is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 3.4 GB, and produces roughly 14.8 tokens per second. 818 cards in total can run it.

How fast is Nucleotide Transformer on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 1,355 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 783 of the cards that can run Nucleotide Transformer clear that.

How much VRAM does Nucleotide Transformer need?

About 3.4 GB at Q8_0 compression, which is what the smallest card that runs it uses. Less compression needs more: the figures in the memory column above are recalculated for each card, because each one holds the least-compressed version it can.

Can I run Nucleotide Transformer on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 3.4 GB and generating roughly 252 tokens per second — a comfortable fit.

Can I run Nucleotide Transformer on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 3.4 GB and generating roughly 155 tokens per second — a comfortable fit.

Can I run Nucleotide Transformer on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 3.4 GB and generating roughly 191 tokens per second — a comfortable fit.

Can I run Nucleotide Transformer on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 3.4 GB and generating roughly 227 tokens per second — a comfortable fit.

Is Nucleotide Transformer open source?

Its weights are published, so Nucleotide Transformer can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.

How many parameters does Nucleotide Transformer have?

Nucleotide Transformer has 2.5B parameters. "We built four distinct foundation language models of different sizes, ranging from 500M up to 2.5B 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 Nucleotide Transformer?

Nucleotide Transformer was published by NVIDIA,Technical University of Munich,InstaDeep, based in United States of America, categorised as industry,Academia,Industry.

Source

Original publication

Record last updated 28 November 2025

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