Ovis2.5 9B TPS calculator

Open weights Alibaba 9B parameters August 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 of 818 cards that can run it

Smallest card that fits

Quadro 6000

6 GB · Q3_K_M · 15.5 tok/s

Fastest card

B200

376 tok/s · 180 GB

Which GPUs can run Ovis2.5 9B?

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
376 tok/s 226–602 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	10.3 GB	Q8_0	Comfortable
376 tok/s 226–602 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	10.3 GB	Q8_0	Comfortable
301 tok/s 180–481 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	10.3 GB	Q8_0	Comfortable
301 tok/s 180–481 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	10.3 GB	Q8_0	Comfortable
240 tok/s 144–385 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	10.3 GB	Q8_0	Comfortable
230 tok/s 138–368 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	10.3 GB	Q8_0	Comfortable
230 tok/s 138–368 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	10.3 GB	Q8_0	Comfortable
220 tok/s 132–352 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	10.3 GB	Q8_0	Comfortable
195 tok/s 117–313 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	10.3 GB	Q8_0	Comfortable
195 tok/s 117–313 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	10.3 GB	Q8_0	Comfortable
195 tok/s 117–313 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	10.3 GB	Q8_0	Comfortable
185 tok/s 111–297 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	10.3 GB	Q8_0	Comfortable
158 tok/s 95–253 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	10.3 GB	Q8_0	Comfortable
158 tok/s 95–253 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	10.3 GB	Q8_0	Comfortable
158 tok/s 95–253 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	10.3 GB	Q8_0	Comfortable
158 tok/s 95–253 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	10.3 GB	Q8_0	Comfortable
158 tok/s 95–253 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	10.3 GB	Q8_0	Comfortable
125 tok/s 75–200 · low confidence	CMP 170HX 8 GB NVIDIA	8 GB	1,490 GB/s	Sep 2021	7.2 GB	Q5_K_M	Tight
120 tok/s 72–193 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	10.3 GB	Q8_0	Comfortable
120 tok/s 72–193 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	10.3 GB	Q8_0	Comfortable
107 tok/s 64–171 · low confidence	CMP 170HX 10 GB NVIDIA	10 GB	1,560 GB/s	Sep 2021	8.2 GB	Q6_K	Tight
100 tok/s 60–161 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	10.3 GB	Q8_0	Comfortable
98.2 tok/s 59–157 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	10.3 GB	Q8_0	Comfortable
96.0 tok/s 58–154 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	10.3 GB	Q8_0	Comfortable
96.0 tok/s 58–154 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	10.3 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: Alibaba
Organisation type: Industry
Country: China
Published: 15 August 2025
Authors: Shiyin Lu, Yang Li, Yu Xia, Yuwei Hu, Shanshan Zhao, Yanqing Ma, Zhichao Wei, Yinglun Li, Lunhao Duan, Jianshan Zhao, Yuxuan Han, Haijun Li, Wanying Chen, Junke Tang, Chengkun Hou, Zhixing Du, Tianli Zhou, Wenjie Zhang, Huping Ding, Jiahe Li, Wen Li, Gui Hu, Yiliang Gu, Siran Yang, Jiamang Wang, Hailong Sun, Yibo Wang, Hui Sun, Jinlong Huang, Yuping He, Shengze Shi, Weihong Zhang, Guodong Zheng, J…

What it does

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

Domain: Multimodal, Language, Vision, Video
Task: Language modeling/generation, Visual question answering, Question answering, Character recognition (OCR), Image captioning, Quantitative reasoning, Mathematical reasoning, Video description
Base model: Qwen3-8B

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: 9B
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 (unrestricted)
Training code: Unreleased
Hugging Face: AIDC-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: Ovis2.5 Technical Report
Last updated: 28 November 2025

The extremes

The ten fastest GPUs for Ovis2.5 9B

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 Ovis2.5 9B

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

376 tok/s

Ovis2.5 9B is small enough at 9B 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, Q3_K_M compression, roughly 15.5 tokens per second.

The quickest result comes from a B200 at around 376 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

About this model

Ovis2.5 9B was published by Alibaba, in China, in August 2025. The organisation is categorised as industry.

It works in Multimodal, Language, Vision, Video, and is recorded as doing language modeling/generation, Visual question answering, Question answering, Character recognition (OCR), Image captioning, Quantitative reasoning, Mathematical reasoning, Video description.

Its starting point was Qwen3-8B — most models at this scale are adapted from an existing base rather than built from nothing.

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 AIDC-AI organisation on Hugging Face.

How fast it runs, and why

Half the cards that hold it manage more than 21.1 tokens per second, and 541 exceed reading speed outright.

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 Ovis2.5 9B

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 Ovis2.5 9B actually needs — around 5.1 GB at Q3_K_M. No amount of processing power compensates for a card that cannot hold it.
02

Decide how long your conversations run

The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context Ovis2.5 9B can slip off a card that handles short questions easily.
03

Decide how much compression you will accept

Compression is what makes Ovis2.5 9B 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

Ranking by tokens per second for Ovis2.5 9B follows memory bandwidth, not core counts, which is why the B200 tops it at 376 tok/s.
05

Look at the headroom, not just the fit

Tight means Ovis2.5 9B 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

Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond Ovis2.5 9B.

Answers

Ovis2.5 9B — common questions

Who created Ovis2.5 9B?

Ovis2.5 9B was published by Alibaba, based in China, categorised as industry.

When was Ovis2.5 9B released?

Ovis2.5 9B was published in August 2025.

What is Ovis2.5 9B used for?

Ovis2.5 9B works in Multimodal, Language, Vision, Video, and is recorded as handling language modeling/generation, Visual question answering, Question answering, Character recognition (OCR), Image captioning, Quantitative reasoning, Mathematical reasoning, Video description. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Where can I download Ovis2.5 9B?

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

Can I run Ovis2.5 9B if it does not fit in my GPU?

It can be split between the card and system memory, but Ovis2.5 9B generates painfully slowly that way — the nearest miss we calculate is short by 1.6 GB. Nothing on this page assumes offloading.

Would two GPUs run Ovis2.5 9B faster?

Two cards buy memory rather than speed. That matters for Ovis2.5 9B only if one card cannot hold it — 582 can, so a second adds little.

Why does the quantisation differ between cards for Ovis2.5 9B?

A larger card holds a more accurate copy. Across the cards that run Ovis2.5 9B, 4 compression levels are used; the floor control above pins it to one.

How accurate are these Ovis2.5 9B speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 226–602 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 Ovis2.5 9B?

The smallest card in our catalogue that holds Ovis2.5 9B is the Quadro 6000, with 6 GB of memory. It runs the model at Q3_K_M using about 5.1 GB, and produces roughly 15.5 tokens per second. 582 cards in total can run it.

How fast is Ovis2.5 9B on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 376 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 541 of the cards that can run Ovis2.5 9B clear that.

How much VRAM does Ovis2.5 9B need?

About 5.1 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 Ovis2.5 9B on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q5_K_M, using about 7.2 GB and generating roughly 125 tokens per second — a tight fit.

Can I run Ovis2.5 9B on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 10.3 GB and generating roughly 42.9 tokens per second — a tight fit.

Can I run Ovis2.5 9B on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 10.3 GB and generating roughly 53.2 tokens per second — a comfortable fit.

Can I run Ovis2.5 9B on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 10.3 GB and generating roughly 63.1 tokens per second — a comfortable fit.

Is Ovis2.5 9B open source?

Its weights are published, so Ovis2.5 9B 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 Ovis2.5 9B have?

Ovis2.5 9B has 9B parameters. 9B. 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

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