Apertus 8B TPS calculator

Open weights ETH Zurich,Ecole Polytechnique F´ed´erale de Lausanne (EPFL),Swiss National Supercomputing Centre (CSCS),Swisscom 8B parameters September 2025

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 Apertus 8B?

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

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: ETH Zurich,Ecole Polytechnique F´ed´erale de Lausanne (EPFL),Swiss National Supercomputing Centre (CSCS),Swisscom
Organisation type: Academia,Academia,Academia,Industry
Country: Switzerland
Published: 2 September 2025

What it does

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

Domain: Language
Task: Language modeling/generation, Question answering, Code generation, Translation
Numerical format: BF16

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

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 GH200
Chips used: 4,096
Power draw: 5.6 MW

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: swiss-ai

How it is classified

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

Record confidence: Confident

Sources

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

Reference: Apertus: a fully open, transparent, multilingual language model
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Apertus 8B

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 Apertus 8B

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

Quadro 6000

Memory needed

5.1 GB

Fastest

424 tok/s

Apertus 8B 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 entry point is the Quadro 6000: 6 GB of memory, IQ4_XS compression, roughly 15.9 tokens per second.

At the other end, a B200 generates roughly 424 tokens per second on it, on the strength of 8,000 GB/s of memory bandwidth.

Where it came from

Apertus 8B was published by ETH Zurich,Ecole Polytechnique F´ed´erale de Lausanne (EPFL),Swiss National Supercomputing Centre (CSCS),Swisscom, in Switzerland, in September 2025. It comes out of academia,Academia,Academia,Industry.

It works in Language, and is recorded as doing language modeling/generation, Question answering, Code generation, Translation.

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. It is published under the swiss-ai organisation on Hugging Face.

Understanding the speeds

Across every card that can run it, the middle of the range is about 23.8 tokens per second, and 551 of them clear the ten tokens per second that roughly matches reading speed.

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.

Step by step

How to choose a GPU for Apertus 8B

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

The table lists every card that can hold Apertus 8B — around 5.1 GB at IQ4_XS. That figure, not the card's headline performance, is what decides whether it runs.
02

Match the context to your actual use

The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context Apertus 8B can slip off a card that handles short questions easily.
03

Decide how much compression you will accept

The quantisation column varies by card, because a bigger card holds a more accurate copy of Apertus 8B — IQ4_XS on the smallest card that fits. Set a floor to hold the comparison at one level.
04

Sort by speed

The speed ordering for Apertus 8B is effectively an ordering by memory bandwidth, which is why the B200 tops it at 424 tok/s.
05

Look at the headroom, not just the fit

The fit column separates cards that just manage Apertus 8B from those with room to spare. Buy for the second if the context might grow.
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 Apertus 8B alone — a card is usually bought for more than one model.

Answers

Apertus 8B — common questions

Who created Apertus 8B?

Apertus 8B was published by ETH Zurich,Ecole Polytechnique F´ed´erale de Lausanne (EPFL),Swiss National Supercomputing Centre (CSCS),Swisscom, based in Switzerland, categorised as academia,Academia,Academia,Industry.

When was Apertus 8B released?

Apertus 8B was published in September 2025.

What is Apertus 8B used for?

Apertus 8B works in Language, and is recorded as handling language modeling/generation, Question answering, Code generation, Translation. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download Apertus 8B?

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

Can I run Apertus 8B if it does not fit in my GPU?

It can be split between the card and system memory, but Apertus 8B generates painfully slowly that way — the nearest miss we calculate is short by 1.0 GB. Nothing on this page assumes offloading.

Would two GPUs run Apertus 8B faster?

A second card roughly doubles the memory available but not the generation rate. With 582 cards already able to run Apertus 8B alone, the case for pairing is weak.

Why does the quantisation differ between cards for Apertus 8B?

Each card is shown running the least-compressed copy it can hold, and Apertus 8B appears at 4 different compression levels across the cards that fit it. Bigger cards get the more accurate version.

How accurate are these Apertus 8B 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 254–678 tok/s on the B200 rather than a single number.

What GPU do I need to run Apertus 8B?

The smallest card in our catalogue that holds Apertus 8B 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 Apertus 8B 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 Apertus 8B clear that.

How much VRAM does Apertus 8B 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 Apertus 8B 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.

Can I run Apertus 8B 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 Apertus 8B 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 Apertus 8B 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 Apertus 8B open source?

Its weights are published, so Apertus 8B 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 Apertus 8B have?

Apertus 8B 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.

Source

Original publication

Record last updated 28 November 2025

The other direction

Looking at it from the other side?

This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.

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