Aya Vision 32B TPS calculator

Open weights Cohere 33.1B parameters March 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

132 cards that can run it

818 cards we hold specifications for

Smallest card that fits

RTX A4500

20 GB · Q3_K_M · 22.1 tok/s

Fastest card

B200

102 tok/s · 180 GB

Which GPUs can run Aya Vision 32B?

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

132 cards match

Calculating

					Needs	Quantisation	Fit
102 tok/s 61–164 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	36.1 GB	Q8_0	Comfortable
102 tok/s 61–164 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	36.1 GB	Q8_0	Comfortable
81.7 tok/s 49–131 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	36.1 GB	Q8_0	Comfortable
81.7 tok/s 49–131 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	36.1 GB	Q8_0	Comfortable
65.4 tok/s 39–105 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	36.1 GB	Q8_0	Comfortable
62.6 tok/s 38–100 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	36.1 GB	Q8_0	Comfortable
62.6 tok/s 38–100 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	36.1 GB	Q8_0	Comfortable
59.9 tok/s 36–96 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	36.1 GB	Q8_0	Comfortable
53.2 tok/s 32–85 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	36.1 GB	Q8_0	Comfortable
53.2 tok/s 32–85 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	36.1 GB	Q8_0	Comfortable
53.2 tok/s 32–85 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	36.1 GB	Q8_0	Comfortable
50.4 tok/s 30–81 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	36.1 GB	Q8_0	Comfortable
43.0 tok/s 26–69 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	36.1 GB	Q8_0	Comfortable
43.0 tok/s 26–69 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	36.1 GB	Q8_0	Comfortable
43.0 tok/s 26–69 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	36.1 GB	Q8_0	Comfortable
43.0 tok/s 26–69 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	36.1 GB	Q8_0	Comfortable
43.0 tok/s 26–69 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	36.1 GB	Q8_0	Comfortable
39.6 tok/s 24–63 · low confidence	GeForce RTX 5090 D V2 NVIDIA	24 GB	1,340 GB/s	Aug 2025	20.7 GB	Q4_K_M	Tight
36.0 tok/s 22–58 · low confidence	A30X NVIDIA	24 GB	1,220 GB/s	Apr 2021	20.7 GB	Q4_K_M	Tight
34.8 tok/s 21–56 · low confidence	DRIVE A100 PROD NVIDIA	32 GB	1,870 GB/s	May 2020	28.4 GB	Q6_K	Tight
34.8 tok/s 21–56 · low confidence	GRID A100A NVIDIA	32 GB	1,870 GB/s	May 2020	28.4 GB	Q6_K	Tight
33.3 tok/s 20–53 · low confidence	GeForce RTX 5090 NVIDIA	32 GB	1,790 GB/s	Jan 2025	28.4 GB	Q6_K	Tight
33.3 tok/s 20–53 · low confidence	GeForce RTX 5090 D NVIDIA	32 GB	1,790 GB/s	Jan 2025	28.4 GB	Q6_K	Tight
32.7 tok/s 20–52 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	36.1 GB	Q8_0	Comfortable
32.7 tok/s 20–52 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	36.1 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: Cohere
Organisation type: Industry
Country: Canada
Published: 3 March 2025
Authors: Cohere Labs Team

What it does

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

Domain: Multimodal, Vision, Language
Task: Language generation, Image captioning, Visual question answering, Language modeling/generation, Question answering
Base model: Aya Expanse 32B

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: 33.1B
Training data: 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 (non-commercial)
Training code: Unreleased
Hugging Face: CohereLabs

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: Aya Vision: Expanding the worlds AI can see
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Aya Vision 32B

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 Aya Vision 32B

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

The hardware side

Minimum card

RTX A4500

Memory needed

16.9 GB

Fastest

102 tok/s

With 33.1B parameters, Aya Vision 32B lands in the range a serious desktop card can handle once the weights are compressed. 132 of the cards we track can run it.

At the low end, a RTX A4500 handles it — 20 GB, at Q3_K_M, for about 22.1 tokens per second.

At the other end, a B200 generates roughly 102 tokens per second on it, on the strength of 8,000 GB/s of memory bandwidth.

Where it came from

Aya Vision 32B was published by Cohere, in Canada, in March 2025. The organisation is categorised as industry.

It works in Multimodal, Vision, Language, and is recorded as doing language generation, Image captioning, Visual question answering, Language modeling/generation, Question answering.

Its starting point was Aya Expanse 32B — most models at this scale are adapted from an existing base rather than built from nothing.

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 CohereLabs organisation on Hugging Face.

Understanding the speeds

Across every card that can run it, the middle of the range is about 20.3 tokens per second, and 101 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.

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 Aya Vision 32B

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

01

Start from the memory column

The table lists every card that can hold Aya Vision 32B — around 16.9 GB at Q3_K_M. That figure, not the card's headline performance, is what decides whether it runs.
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 Aya Vision 32B stops fitting a card that seemed fine.
03

Choose how far you will compress it

Compression is what makes Aya Vision 32B fit smaller cards, at some cost in accuracy — Q3_K_M on the smallest card that fits. A minimum quality removes the ones that go too far.
04

Sort by speed

Sort by speed to see how cards rank for Aya Vision 32B. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 102 tok/s.
05

Check the fit verdict before buying

Tight means Aya Vision 32B 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

See what else that card runs

Every card name links to its own page, which runs the same calculation across the whole model catalogue. Worth a look before buying for Aya Vision 32B alone — a card is usually bought for more than one model.

Answers

Aya Vision 32B — common questions

How many parameters does Aya Vision 32B have?

Aya Vision 32B has 33.1B parameters. According to huggingface.co/CohereLabs/aya-vision-32b. 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 Aya Vision 32B?

Aya Vision 32B was published by Cohere, based in Canada, categorised as industry.

When was Aya Vision 32B released?

Aya Vision 32B was published in March 2025.

What is Aya Vision 32B used for?

Aya Vision 32B works in Multimodal, Vision, Language, and is recorded as handling language generation, Image captioning, Visual question answering, Language modeling/generation, Question answering. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download Aya Vision 32B?

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

Can I run Aya Vision 32B if it does not fit in my GPU?

It can be split between the card and system memory, but Aya Vision 32B generates painfully slowly that way — the nearest miss we calculate is short by 6.3 GB. Nothing on this page assumes offloading.

Would two GPUs run Aya Vision 32B faster?

A second card roughly doubles the memory available but not the generation rate. With 132 cards already able to run Aya Vision 32B alone, the case for pairing is weak.

Why does the quantisation differ between cards for Aya Vision 32B?

Each card is shown running the least-compressed copy it can hold, and Aya Vision 32B appears at 5 different compression levels across the cards that fit it. Bigger cards get the more accurate version.

How accurate are these Aya Vision 32B speed estimates?

These are estimates with real error bars. The fastest result here, 61–164 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 Aya Vision 32B?

The smallest card in our catalogue that holds Aya Vision 32B is the RTX A4500, with 20 GB of memory. It runs the model at Q3_K_M using about 16.9 GB, and produces roughly 22.1 tokens per second. 132 cards in total can run it.

How fast is Aya Vision 32B on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 102 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 101 of the cards that can run Aya Vision 32B clear that.

How much VRAM does Aya Vision 32B need?

About 16.9 GB at Q3_K_M 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 Aya Vision 32B on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q4_K_M, using about 20.7 GB and generating roughly 39.6 tokens per second — a tight fit.

Is Aya Vision 32B open source?

Its weights are published, so Aya Vision 32B 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.

Source

Original publication

Record last updated 28 November 2025

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