PlasmidGPT TPS calculator

Open weights Harvard University 110M parameters October 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 · 335 tok/s

Fastest card

B200

30,802 tok/s · 180 GB

Which GPUs can run PlasmidGPT?

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
30,802 tok/s 18,481–49,283 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	0.8 GB	Q8_0	Comfortable
30,802 tok/s 18,481–49,283 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	0.8 GB	Q8_0	Comfortable
24,596 tok/s 14,758–39,354 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	0.8 GB	Q8_0	Comfortable
24,596 tok/s 14,758–39,354 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	0.8 GB	Q8_0	Comfortable
19,671 tok/s 11,803–31,474 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	0.8 GB	Q8_0	Comfortable
18,828 tok/s 11,297–30,124 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	0.8 GB	Q8_0	Comfortable
18,828 tok/s 11,297–30,124 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	0.8 GB	Q8_0	Comfortable
18,019 tok/s 10,812–28,831 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	0.8 GB	Q8_0	Comfortable
15,992 tok/s 9,595–25,587 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	0.8 GB	Q8_0	Comfortable
15,992 tok/s 9,595–25,587 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	0.8 GB	Q8_0	Comfortable
15,992 tok/s 9,595–25,587 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	0.8 GB	Q8_0	Comfortable
15,170 tok/s 9,102–24,272 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	0.8 GB	Q8_0	Comfortable
12,937 tok/s 7,762–20,699 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	0.8 GB	Q8_0	Comfortable
12,937 tok/s 7,762–20,699 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	0.8 GB	Q8_0	Comfortable
12,937 tok/s 7,762–20,699 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	0.8 GB	Q8_0	Comfortable
12,937 tok/s 7,762–20,699 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	0.8 GB	Q8_0	Comfortable
12,937 tok/s 7,762–20,699 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	0.8 GB	Q8_0	Comfortable
9,851 tok/s 5,910–15,761 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	0.8 GB	Q8_0	Comfortable
9,851 tok/s 5,910–15,761 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	0.8 GB	Q8_0	Comfortable
8,209 tok/s 4,925–13,134 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	0.8 GB	Q8_0	Comfortable
8,034 tok/s 4,820–12,854 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	0.8 GB	Q8_0	Comfortable
7,855 tok/s 4,713–12,567 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	0.8 GB	Q8_0	Comfortable
7,855 tok/s 4,713–12,567 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	0.8 GB	Q8_0	Comfortable
7,855 tok/s 4,713–12,567 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	0.8 GB	Q8_0	Comfortable
7,855 tok/s 4,713–12,567 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	0.8 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: Harvard University
Organisation type: Academia
Country: United States of America
Published: 1 October 2024
Authors: Bin Shao

What it does

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

Domain: Biology
Task: Plasmid Design

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: 110M
Training data: 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.

How it was established: Operation counting

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: 1
Power draw: 433 W

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)

How it is classified

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

Record confidence: Likely

Sources

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

Reference: PlasmidGPT: a generative framework for plasmid design and annotation
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run PlasmidGPT

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 PlasmidGPT

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

Hardware requirements in practice

Minimum card

Tesla C1080

Memory needed

0.8 GB

Fastest

30,802 tok/s

PlasmidGPT is small enough at 110M parameters that hardware is rarely the obstacle — 818 of the cards we track can run it, including cards several years old.

The entry point is the Tesla C1080: 4 GB of memory, Q8_0 compression, roughly 335 tokens per second.

Top of the range is the B200, at roughly 30,802 tokens per second thanks to 8,000 GB/s of bandwidth.

About this model

PlasmidGPT was published by Harvard University, in United States of America, in October 2024. academia is the category the publisher falls under.

It works in Biology, and is recorded as doing plasmid Design.

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

How fast it runs, and why

Half the cards that hold it manage more than 864.9 tokens per second, and 818 exceed reading speed outright.

Every weight participates in every token here, so bandwidth is the whole story: the ranking below is effectively a ranking of memory throughput.

Its internal architecture is not on file, so memory is approximated from the parameter count and marked accordingly. Expect the real figure to differ, more so at long context.

Step by step

How to choose a GPU for PlasmidGPT

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

01

Check what it needs before anything else

Look at what PlasmidGPT actually needs — around 0.8 GB at Q8_0. No amount of processing power compensates for a card that cannot hold it.
02

Match the context to your actual use

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

Choose how far you will compress it

Compression is what makes PlasmidGPT 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

Compare tokens per second, not specifications

Ranking by tokens per second for PlasmidGPT follows memory bandwidth, not core counts, which is why the B200 tops it at 30,802 tok/s.
05

Check the fit verdict before buying

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

See what else that card runs

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

Answers

PlasmidGPT — common questions

Is PlasmidGPT open source?

Its weights are published, so PlasmidGPT 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 PlasmidGPT have?

PlasmidGPT has 110M parameters. "The model consists of 12 230 layers with a dimension of 512, with 8 attention heads and a total of 110M 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 PlasmidGPT?

PlasmidGPT was published by Harvard University, based in United States of America, categorised as academia.

When was PlasmidGPT released?

PlasmidGPT was published in October 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 PlasmidGPT used for?

PlasmidGPT works in Biology, and is recorded as handling plasmid Design. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Where can I download PlasmidGPT?

The weights for PlasmidGPT 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 PlasmidGPT if it does not fit in my GPU?

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

Would two GPUs run PlasmidGPT faster?

A second card roughly doubles the memory available but not the generation rate. With 818 cards already able to run PlasmidGPT alone, the case for pairing is weak.

Why does the quantisation differ between cards for PlasmidGPT?

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

How accurate are these PlasmidGPT speed estimates?

These are estimates with real error bars. The fastest result here, 18,481–49,283 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 PlasmidGPT?

The smallest card in our catalogue that holds PlasmidGPT is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 0.8 GB, and produces roughly 335 tokens per second. 818 cards in total can run it.

How fast is PlasmidGPT on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 30,802 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 818 of the cards that can run PlasmidGPT clear that.

How much VRAM does PlasmidGPT need?

About 0.8 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 PlasmidGPT on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 0.8 GB and generating roughly 5,737 tokens per second — a comfortable fit.

Can I run PlasmidGPT on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 0.8 GB and generating roughly 3,513 tokens per second — a comfortable fit.

Can I run PlasmidGPT on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 0.8 GB and generating roughly 4,351 tokens per second — a comfortable fit.

Can I run PlasmidGPT on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 0.8 GB and generating roughly 5,159 tokens per second — a comfortable fit.

Source

Original publication

Record last updated 28 November 2025

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