P-LLama3 TPS calculator

Open weights University of Siena 8B parameters August 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

582 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Quadro 6000

6 GB · IQ4_XS · 15.9 tok/s

Fastest card

B200

424 tok/s · 180 GB

Which GPUs can run P-LLama3?

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

582 cards match

Calculating

					Needs	Quantisation	Fit
424 tok/s 254–678 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	9.3 GB	Q8_0	Comfortable
424 tok/s 254–678 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	9.3 GB	Q8_0	Comfortable
338 tok/s 203–541 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	9.3 GB	Q8_0	Comfortable
338 tok/s 203–541 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	9.3 GB	Q8_0	Comfortable
270 tok/s 162–433 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	9.3 GB	Q8_0	Comfortable
259 tok/s 155–414 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	9.3 GB	Q8_0	Comfortable
259 tok/s 155–414 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	9.3 GB	Q8_0	Comfortable
248 tok/s 149–396 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	9.3 GB	Q8_0	Comfortable
220 tok/s 132–352 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	9.3 GB	Q8_0	Comfortable
220 tok/s 132–352 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	9.3 GB	Q8_0	Comfortable
220 tok/s 132–352 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	9.3 GB	Q8_0	Comfortable
209 tok/s 125–334 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	9.3 GB	Q8_0	Comfortable
178 tok/s 107–285 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	9.3 GB	Q8_0	Comfortable
178 tok/s 107–285 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	9.3 GB	Q8_0	Comfortable
178 tok/s 107–285 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	9.3 GB	Q8_0	Comfortable
178 tok/s 107–285 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	9.3 GB	Q8_0	Comfortable
178 tok/s 107–285 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	9.3 GB	Q8_0	Comfortable
141 tok/s 85–225 · low confidence	CMP 170HX 8 GB NVIDIA	8 GB	1,490 GB/s	Sep 2021	6.5 GB	Q5_K_M	Tight
135 tok/s 81–217 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	9.3 GB	Q8_0	Comfortable
135 tok/s 81–217 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	9.3 GB	Q8_0	Comfortable
120 tok/s 72–192 · low confidence	CMP 170HX 10 GB NVIDIA	10 GB	1,560 GB/s	Sep 2021	7.4 GB	Q6_K	Comfortable
113 tok/s 68–181 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	9.3 GB	Q8_0	Comfortable
110 tok/s 66–177 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	9.3 GB	Q8_0	Comfortable
108 tok/s 65–173 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	9.3 GB	Q8_0	Comfortable
108 tok/s 65–173 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	9.3 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: University of Siena
Organisation type: Academia
Country: Italy
Published: 12 August 2024
Authors: Kamyar Zeinalipour, Neda Jamshidi, Monica Bianchini, Marco Maggini, Marco Gori

What it does

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

Domain: Biology
Task: Protein generation, Protein design
Base model: Llama 3-8B

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: 8B
Training data: tokens
Epochs: 0.76

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: 7.2 × 10²³ FLOP
How it was established: Operation counting
Fine-tuning compute: 7.9 × 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 RTX A6000
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 (restricted use)
Training code: Unreleased
Hugging Face: Kamyar-zeinalipour

How it is classified

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

Likely above 10²³ FLOP: Yes
Record confidence: Confident
Citations: 2

Sources

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

Reference: Design Proteins Using Large Language Models: Enhancements and Comparative Analyses
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run P-LLama3

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 P-LLama3

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 it takes to run this model

Minimum card

Quadro 6000

Memory needed

5.1 GB

Fastest

424 tok/s

P-LLama3 is small enough at 8B parameters that hardware is rarely the obstacle — 582 of the cards we track can run it, including cards several years old.

The smallest card that holds it is the Quadro 6000 with 6 GB, running it at IQ4_XS and producing around 15.9 tokens per second.

The quickest result comes from a B200 at around 424 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

About this model

P-LLama3 was published by University of Siena, in Italy, in August 2024. It comes out of academia.

It works in Biology, and is recorded as doing protein generation, Protein design.

It is derived from Llama 3-8B rather than trained from scratch, which is the usual way a specialised model is produced.

Published weights mean the model runs on your machine rather than someone else's, which is what makes the hardware question below answerable at all. It is published under the Kamyar-zeinalipour organisation on Hugging Face.

How fast it runs, and why

The median result is around 23.8 tokens per second; 551 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.

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.

Training and provenance

Training it took roughly 7.2 × 10²³ FLOP of computation, on NVIDIA RTX A6000 — a measure of what producing the model cost, not of how fast it answers.

Step by step

How to choose a GPU for P-LLama3

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

The table lists every card that can hold P-LLama3 — around 5.1 GB at IQ4_XS. That figure, not the card's headline performance, is what decides whether it runs.
02

Set the context length you will work at

Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for P-LLama3.
03

Decide how much compression you will accept

Compression is what makes P-LLama3 fit smaller cards, at some cost in accuracy — IQ4_XS on the smallest card that fits. A minimum quality removes the ones that go too far.
04

Rank by throughput rather than spec sheet

Ranking by tokens per second for P-LLama3 follows memory bandwidth, not core counts, which is why the B200 tops it at 424 tok/s.
05

Read the fit column last

Tight means P-LLama3 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

Every card name links to its own page, which runs the same calculation across the whole model catalogue. Worth a look before buying for P-LLama3 alone — a card is usually bought for more than one model.

Answers

P-LLama3 — common questions

Can I run P-LLama3 on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 9.3 GB and generating roughly 48.3 tokens per second — a tight fit.

Can I run P-LLama3 on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 9.3 GB and generating roughly 59.8 tokens per second — a comfortable fit.

Can I run P-LLama3 on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 9.3 GB and generating roughly 70.9 tokens per second — a comfortable fit.

Is P-LLama3 open source?

Its weights are published, so P-LLama3 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 P-LLama3 have?

P-LLama3 has 8B parameters. 8B. 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 P-LLama3?

P-LLama3 was published by University of Siena, based in Italy, categorised as academia.

When was P-LLama3 released?

P-LLama3 was published in August 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 P-LLama3 used for?

P-LLama3 works in Biology, and is recorded as handling protein generation, Protein design. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.

Where can I download P-LLama3?

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

How much compute was used to train P-LLama3?

Around 7.2 × 10²³ FLOP, on NVIDIA RTX A6000. 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 P-LLama3 if it does not fit in my GPU?

Partly. Layers that do not fit sit in system memory and run at a fraction of the speed, so a mostly-offloaded P-LLama3 is rarely worth using — the nearest miss we calculate is short by 1.0 GB. Every figure here assumes the whole model is on the card.

Would two GPUs run P-LLama3 faster?

Capacity adds across cards; throughput does not. Since 582 of the cards we track already hold P-LLama3 on their own, a second card is rarely the answer here.

Why does the quantisation differ between cards for P-LLama3?

Because capacity varies, so does how hard P-LLama3 has to be squeezed — 4 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these P-LLama3 speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 254–678 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 P-LLama3?

The smallest card in our catalogue that holds P-LLama3 is the Quadro 6000, with 6 GB of memory. It runs the model at IQ4_XS using about 5.1 GB, and produces roughly 15.9 tokens per second. 582 cards in total can run it.

How fast is P-LLama3 on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 424 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 551 of the cards that can run P-LLama3 clear that.

How much VRAM does P-LLama3 need?

About 5.1 GB at IQ4_XS 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 P-LLama3 on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q5_K_M, using about 6.5 GB and generating roughly 141 tokens per second — a tight fit.

Source

Original publication

Record last updated 25 May 2026

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