ProtRNA TPS calculator

Open weights Fudan University,Shanghai AI Lab 650M parameters September 2024

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 · 56.7 tok/s

Fastest card

B200

5,213 tok/s · 180 GB

Which GPUs can run ProtRNA?

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
5,213 tok/s 3,128–8,340 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	1.4 GB	Q8_0	Comfortable
5,213 tok/s 3,128–8,340 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	1.4 GB	Q8_0	Comfortable
4,162 tok/s 2,497–6,660 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	1.4 GB	Q8_0	Comfortable
4,162 tok/s 2,497–6,660 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	1.4 GB	Q8_0	Comfortable
3,329 tok/s 1,997–5,326 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	1.4 GB	Q8_0	Comfortable
3,186 tok/s 1,912–5,098 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.4 GB	Q8_0	Comfortable
3,186 tok/s 1,912–5,098 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.4 GB	Q8_0	Comfortable
3,049 tok/s 1,830–4,879 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	1.4 GB	Q8_0	Comfortable
2,706 tok/s 1,624–4,330 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	1.4 GB	Q8_0	Comfortable
2,706 tok/s 1,624–4,330 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	1.4 GB	Q8_0	Comfortable
2,706 tok/s 1,624–4,330 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	1.4 GB	Q8_0	Comfortable
2,567 tok/s 1,540–4,108 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	1.4 GB	Q8_0	Comfortable
2,189 tok/s 1,314–3,503 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.4 GB	Q8_0	Comfortable
2,189 tok/s 1,314–3,503 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	1.4 GB	Q8_0	Comfortable
2,189 tok/s 1,314–3,503 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	1.4 GB	Q8_0	Comfortable
2,189 tok/s 1,314–3,503 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.4 GB	Q8_0	Comfortable
2,189 tok/s 1,314–3,503 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	1.4 GB	Q8_0	Comfortable
1,667 tok/s 1,000–2,667 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	1.4 GB	Q8_0	Comfortable
1,667 tok/s 1,000–2,667 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	1.4 GB	Q8_0	Comfortable
1,389 tok/s 834–2,223 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	1.4 GB	Q8_0	Comfortable
1,360 tok/s 816–2,175 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	1.4 GB	Q8_0	Comfortable
1,329 tok/s 798–2,127 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	1.4 GB	Q8_0	Comfortable
1,329 tok/s 798–2,127 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	1.4 GB	Q8_0	Comfortable
1,329 tok/s 798–2,127 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	1.4 GB	Q8_0	Comfortable
1,329 tok/s 798–2,127 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	1.4 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: Fudan University,Shanghai AI Lab
Organisation type: Academia,Academia
Country: China
Published: 14 September 2024
Authors: Ruoxi Zhang, Ben Ma, Gang Xu, Jianpeng Ma

What it does

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

Domain: Biology
Task: RNA structure prediction, RNA-Protein interaction prediction, Mean ribosome load prediction
Base model: ESM2-650M

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: 650M
Training data: tokens
Epochs: 6

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 V100
Chips used: 4
Power draw: 2.4 kW

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: Unreleased

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: 2

Sources

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

Reference: ProtRNA: A Protein-derived RNA Language Model by Cross-Modality Transfer Learning
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run ProtRNA

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 ProtRNA

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

1.4 GB

Fastest

5,213 tok/s

ProtRNA is small enough at 650M 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 56.7 tokens per second.

The quickest result comes from a B200 at around 5,213 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

Background

ProtRNA was published by Fudan University,Shanghai AI Lab, in China, in September 2024. It comes out of academia,Academia.

It works in Biology, and is recorded as doing rNA structure prediction, RNA-Protein interaction prediction, Mean ribosome load prediction.

Its starting point was ESM2-650M — most models at this scale are adapted from an existing base rather than built from nothing.

Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded.

Reading the throughput figures

Across every card that can run it, the middle of the range is about 146.4 tokens per second, and 809 of them clear the ten tokens per second that roughly matches reading speed.

It is a dense model, so every parameter is read for every token produced. That makes speed track memory bandwidth almost exactly — a card with twice the bandwidth generates roughly twice as fast.

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.

Step by step

How to choose a GPU for ProtRNA

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 ProtRNA — around 1.4 GB at Q8_0. Capacity is the gate — a card either holds it or it does not.
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 ProtRNA can slip off a card that handles short questions easily.
03

Set a quality floor

The quantisation column varies by card, because a bigger card holds a more accurate copy of ProtRNA — 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

Ranking by tokens per second for ProtRNA follows memory bandwidth, not core counts, which is why the B200 tops it at 5,213 tok/s.
05

Check the fit verdict before buying

Tight means ProtRNA 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

Open the card you have settled on

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

Answers

ProtRNA — common questions

Is ProtRNA open source?

Its weights are published, so ProtRNA 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 ProtRNA have?

ProtRNA has 650M 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 ProtRNA?

ProtRNA was published by Fudan University,Shanghai AI Lab, based in China, categorised as academia,Academia.

When was ProtRNA released?

ProtRNA 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 ProtRNA used for?

ProtRNA works in Biology, and is recorded as handling rNA structure prediction, RNA-Protein interaction prediction, Mean ribosome load prediction. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download ProtRNA?

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

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

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

Would two GPUs run ProtRNA faster?

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

Why does the quantisation differ between cards for ProtRNA?

Each card is shown running the least-compressed copy it can hold, and ProtRNA appears at 1 different compression levels across the cards that fit it. Bigger cards get the more accurate version.

How accurate are these ProtRNA speed estimates?

Every figure is derived from memory bandwidth and model size, not benchmarked. That is why each is published as a range such as 3,128–8,340 tok/s on the B200 rather than a single number.

What GPU do I need to run ProtRNA?

The smallest card in our catalogue that holds ProtRNA is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 1.4 GB, and produces roughly 56.7 tokens per second. 818 cards in total can run it.

How fast is ProtRNA on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 5,213 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 809 of the cards that can run ProtRNA clear that.

How much VRAM does ProtRNA need?

About 1.4 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 ProtRNA on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 1.4 GB and generating roughly 971 tokens per second — a comfortable fit.

Can I run ProtRNA on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 1.4 GB and generating roughly 595 tokens per second — a comfortable fit.

Can I run ProtRNA on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 1.4 GB and generating roughly 736 tokens per second — a comfortable fit.

Can I run ProtRNA on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 1.4 GB and generating roughly 873 tokens per second — a comfortable fit.

Source

Original publication

Record last updated 28 November 2025

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