OntoProtein TPS calculator

Open weights Zhejiang University (ZJU) 420M parameters January 2022

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 · 87.8 tok/s

Fastest card

B200

8,067 tok/s · 180 GB

Which GPUs can run OntoProtein?

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.

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Card memory

Also show cards that cannot run it

818 cards match

Calculating

					Needs	Quantisation	Fit
8,067 tok/s 4,840–12,908 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	1.1 GB	Q8_0	Comfortable
8,067 tok/s 4,840–12,908 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	1.1 GB	Q8_0	Comfortable
6,442 tok/s 3,865–10,307 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	1.1 GB	Q8_0	Comfortable
6,442 tok/s 3,865–10,307 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	1.1 GB	Q8_0	Comfortable
5,152 tok/s 3,091–8,243 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	1.1 GB	Q8_0	Comfortable
4,931 tok/s 2,959–7,890 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.1 GB	Q8_0	Comfortable
4,931 tok/s 2,959–7,890 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.1 GB	Q8_0	Comfortable
4,719 tok/s 2,832–7,551 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	1.1 GB	Q8_0	Comfortable
4,188 tok/s 2,513–6,701 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	1.1 GB	Q8_0	Comfortable
4,188 tok/s 2,513–6,701 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	1.1 GB	Q8_0	Comfortable
4,188 tok/s 2,513–6,701 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	1.1 GB	Q8_0	Comfortable
3,973 tok/s 2,384–6,357 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	1.1 GB	Q8_0	Comfortable
3,388 tok/s 2,033–5,421 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.1 GB	Q8_0	Comfortable
3,388 tok/s 2,033–5,421 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	1.1 GB	Q8_0	Comfortable
3,388 tok/s 2,033–5,421 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	1.1 GB	Q8_0	Comfortable
3,388 tok/s 2,033–5,421 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.1 GB	Q8_0	Comfortable
3,388 tok/s 2,033–5,421 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	1.1 GB	Q8_0	Comfortable
2,580 tok/s 1,548–4,128 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	1.1 GB	Q8_0	Comfortable
2,580 tok/s 1,548–4,128 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	1.1 GB	Q8_0	Comfortable
2,150 tok/s 1,290–3,440 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	1.1 GB	Q8_0	Comfortable
2,104 tok/s 1,262–3,366 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	1.1 GB	Q8_0	Comfortable
2,057 tok/s 1,234–3,291 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	1.1 GB	Q8_0	Comfortable
2,057 tok/s 1,234–3,291 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	1.1 GB	Q8_0	Comfortable
2,057 tok/s 1,234–3,291 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	1.1 GB	Q8_0	Comfortable
2,057 tok/s 1,234–3,291 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	1.1 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: Zhejiang University (ZJU)
Organisation type: Academia
Country: China
Published: 23 January 2022
Authors: Ningyu Zhang, Zhen Bi, Xiaozhuan Liang, Siyuan Cheng, Shumin Deng, Qiang Zhang, Jiazhang Lian, Huajun Chen, Haosen Hong

What it does

The problem areas the model was built for. A model can carry several of each.

Domain: Biology, Language
Task: Proteins, Protein or nucleotide language model (pLM/nLM), Protein interaction prediction, Protein function prediction, Protein representation learning
Base model: ProtBERT-BFD
Numerical format: FP16

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: 420M
Training data: 2,868,520,960 tokens

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 (unrestricted)
Training code: Open source
Hugging Face: zjunlp

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

Sources

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

Reference: ONTOPROTEIN: PROTEIN PRETRAINING WITH GENE ONTOLOGY EMBEDDING
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run OntoProtein

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 OntoProtein

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

What you need to run it

Minimum card

Tesla C1080

Memory needed

1.1 GB

Fastest

8,067 tok/s

OntoProtein is small enough at 420M parameters that hardware is rarely the obstacle — 818 of the cards we track can run it, including cards several years old.

The least hardware that works is a Tesla C1080. Its 4 GB is enough at Q8_0 compression, giving roughly 87.8 tokens per second.

Top of the range is the B200, at roughly 8,067 tokens per second thanks to 8,000 GB/s of bandwidth.

What this model is

OntoProtein was published by Zhejiang University (ZJU), in China, in January 2022. It comes out of academia.

It works in Biology, Language, and is recorded as doing proteins, Protein or nucleotide language model (pLM/nLM), Protein interaction prediction, Protein function prediction, Protein representation learning.

It builds on ProtBERT-BFD, which is why it shares that model's general shape and size.

The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached. It is published under the zjunlp organisation on Hugging Face.

What decides the speed

The median result is around 226.5 tokens per second; 817 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.

Without the attention layout on record, the memory column is an approximation. It is close enough to choose hardware by, and least reliable at long context.

How it was trained

Around 2,868,520,960 tokens went into training it.

Its inclusion criterion is sOTA improvement.

Step by step

How to choose a GPU for OntoProtein

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

01

Read the memory figure first

Every card here has been checked against OntoProtein — around 1.1 GB at Q8_0. Capacity is the gate — a card either holds it or it does not.
02

Decide how long your conversations run

Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason OntoProtein stops fitting a card that seemed fine.
03

Decide how much compression you will accept

The quantisation column varies by card, because a bigger card holds a more accurate copy of OntoProtein — Q8_0 on the smallest card that fits. Set a floor to hold the comparison at one level.
04

Compare tokens per second, not specifications

Sort by speed to see how cards rank for OntoProtein. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 8,067 tok/s.
05

Check the fit verdict before buying

The fit column separates cards that just manage OntoProtein from those with room to spare. Buy for the second if the context might grow.
06

Check the card from the other side

Following a card through to its own page shows every other model it can hold, which is the question that follows once OntoProtein is settled.

Answers

OntoProtein — common questions

How much VRAM does OntoProtein need?

About 1.1 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 OntoProtein on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 1.1 GB and generating roughly 1,503 tokens per second — a comfortable fit.

Can I run OntoProtein on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 1.1 GB and generating roughly 920 tokens per second — a comfortable fit.

Can I run OntoProtein on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 1.1 GB and generating roughly 1,140 tokens per second — a comfortable fit.

Can I run OntoProtein on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 1.1 GB and generating roughly 1,351 tokens per second — a comfortable fit.

Is OntoProtein open source?

Its weights are published, so OntoProtein 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 OntoProtein have?

OntoProtein has 420M parameters. "For the protein encoder, we use the pre-trained ProtBert from Elnaggar et al. (2020).". 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 OntoProtein?

OntoProtein was published by Zhejiang University (ZJU), based in China, categorised as academia.

When was OntoProtein released?

OntoProtein was published in January 2022. 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 OntoProtein used for?

OntoProtein works in Biology, Language, and is recorded as handling proteins, Protein or nucleotide language model (pLM/nLM), Protein interaction prediction, Protein function prediction, Protein representation learning. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Where can I download OntoProtein?

Its weights are published under the zjunlp organisation on Hugging Face. We do not host model files — this site calculates what hardware is needed to run them.

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

It can be split between the card and system memory, but OntoProtein generates painfully slowly that way. Nothing on this page assumes offloading.

Would two GPUs run OntoProtein faster?

Two cards buy memory rather than speed. That matters for OntoProtein only if one card cannot hold it — 818 can, so a second adds little.

Why does the quantisation differ between cards for OntoProtein?

Because capacity varies, so does how hard OntoProtein has to be squeezed — 1 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these OntoProtein speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 4,840–12,908 tok/s on the B200, for instance. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

What GPU do I need to run OntoProtein?

The smallest card in our catalogue that holds OntoProtein is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 1.1 GB, and produces roughly 87.8 tokens per second. 818 cards in total can run it.

How fast is OntoProtein on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 8,067 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 817 of the cards that can run OntoProtein clear that.

Source

Original publication

Record last updated 28 November 2025

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