mPLUG-Owl2 TPS calculator

Open weights Alibaba 7.1B parameters November 2023

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

589 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Tesla K20c

5 GB · Q3_K_M · 28.4 tok/s

Fastest card

B200

476 tok/s · 180 GB

Which GPUs can run mPLUG-Owl2?

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

589 cards match

Calculating

					Needs	Quantisation	Fit
476 tok/s 286–761 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	8.3 GB	Q8_0	Comfortable
476 tok/s 286–761 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	8.3 GB	Q8_0	Comfortable
380 tok/s 228–608 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	8.3 GB	Q8_0	Comfortable
380 tok/s 228–608 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	8.3 GB	Q8_0	Comfortable
304 tok/s 182–486 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	8.3 GB	Q8_0	Comfortable
291 tok/s 175–465 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	8.3 GB	Q8_0	Comfortable
291 tok/s 175–465 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	8.3 GB	Q8_0	Comfortable
278 tok/s 167–445 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	8.3 GB	Q8_0	Comfortable
247 tok/s 148–395 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	8.3 GB	Q8_0	Comfortable
247 tok/s 148–395 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	8.3 GB	Q8_0	Comfortable
247 tok/s 148–395 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	8.3 GB	Q8_0	Comfortable
234 tok/s 141–375 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	8.3 GB	Q8_0	Comfortable
200 tok/s 120–320 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	8.3 GB	Q8_0	Comfortable
200 tok/s 120–320 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	8.3 GB	Q8_0	Comfortable
200 tok/s 120–320 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	8.3 GB	Q8_0	Comfortable
200 tok/s 120–320 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	8.3 GB	Q8_0	Comfortable
200 tok/s 120–320 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	8.3 GB	Q8_0	Comfortable
152 tok/s 91–244 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	8.3 GB	Q8_0	Comfortable
152 tok/s 91–244 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	8.3 GB	Q8_0	Comfortable
129 tok/s 77–206 · low confidence	CMP 170HX 8 GB NVIDIA	8 GB	1,490 GB/s	Sep 2021	6.7 GB	Q6_K	Tight
127 tok/s 76–203 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	8.3 GB	Q8_0	Comfortable
124 tok/s 74–199 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	8.3 GB	Q8_0	Comfortable
121 tok/s 73–194 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	8.3 GB	Q8_0	Comfortable
121 tok/s 73–194 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	8.3 GB	Q8_0	Comfortable
121 tok/s 73–194 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	8.3 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: 7 November 2023
Authors: Qinghao Ye, Haiyang Xu, Jiabo Ye, Ming Yan, Anwen Hu, Haowei Liu, Qi Qian, Ji Zhang, Fei Huang, Jingren Zhou

What it does

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

Domain: Vision, Language, Multimodal
Task: Visual question answering, Image captioning, Language modeling/generation
Base model: Llama 2-7B
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: 7.1B
Training data: 182,032,793,600 tokens
Epochs: 1

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
Fine-tuning compute: 1.7 × 10¹⁹ FLOP

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: Mizukiluke

How it is classified

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

Why it is tracked: SOTA improvement
Record confidence: Speculative

Sources

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

Reference: mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run mPLUG-Owl2

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 mPLUG-Owl2

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

Tesla K20c

Memory needed

4.2 GB

Fastest

476 tok/s

mPLUG-Owl2 is small enough at 7.1B parameters that hardware is rarely the obstacle — 589 of the cards we track can run it, including cards several years old.

At the low end, a Tesla K20c handles it — 5 GB, at Q3_K_M, for about 28.4 tokens per second.

Top of the range is the B200, at roughly 476 tokens per second thanks to 8,000 GB/s of bandwidth.

Background

mPLUG-Owl2 was published by Alibaba, in China, in November 2023. The organisation is categorised as industry.

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

Its starting point was Llama 2-7B — most models at this scale are adapted from an existing base rather than built from nothing.

Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded. It is published under the Mizukiluke organisation on Hugging Face.

Reading the throughput figures

Half the cards that hold it manage more than 25.7 tokens per second, and 559 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.

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.

What went into building it

It was trained on about 182,032,793,600 tokens of text.

The reason it appears in this catalogue at all is sOTA improvement.

Step by step

How to choose a GPU for mPLUG-Owl2

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 mPLUG-Owl2 actually needs — around 4.2 GB at Q3_K_M. No amount of processing power compensates for a card that cannot hold it.
02

Set the context length you will work at

The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context mPLUG-Owl2 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 mPLUG-Owl2 — Q3_K_M on the smallest card that fits. Set a floor to hold the comparison at one level.
04

Compare tokens per second, not specifications

The speed ordering for mPLUG-Owl2 is effectively an ordering by memory bandwidth, which is why the B200 tops it at 476 tok/s.
05

Check the fit verdict before buying

Tight means mPLUG-Owl2 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

Open the card you have settled on

Every card name links to its own page, which runs the same calculation across the whole model catalogue. Worth a look before buying for mPLUG-Owl2 alone — a card is usually bought for more than one model.

Answers

mPLUG-Owl2 — common questions

Would two GPUs run mPLUG-Owl2 faster?

Capacity adds across cards; throughput does not. Since 589 of the cards we track already hold mPLUG-Owl2 on their own, a second card is rarely the answer here.

Why does the quantisation differ between cards for mPLUG-Owl2?

A larger card holds a more accurate copy. Across the cards that run mPLUG-Owl2, 4 compression levels are used; the floor control above pins it to one.

How accurate are these mPLUG-Owl2 speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 286–761 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 mPLUG-Owl2?

The smallest card in our catalogue that holds mPLUG-Owl2 is the Tesla K20c, with 5 GB of memory. It runs the model at Q3_K_M using about 4.2 GB, and produces roughly 28.4 tokens per second. 589 cards in total can run it.

How fast is mPLUG-Owl2 on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 476 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 559 of the cards that can run mPLUG-Owl2 clear that.

How much VRAM does mPLUG-Owl2 need?

About 4.2 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 mPLUG-Owl2 on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q6_K, using about 6.7 GB and generating roughly 129 tokens per second — a tight fit.

Can I run mPLUG-Owl2 on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 8.3 GB and generating roughly 54.3 tokens per second — a comfortable fit.

Can I run mPLUG-Owl2 on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 8.3 GB and generating roughly 67.2 tokens per second — a comfortable fit.

Can I run mPLUG-Owl2 on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 8.3 GB and generating roughly 79.7 tokens per second — a comfortable fit.

Is mPLUG-Owl2 open source?

Its weights are published, so mPLUG-Owl2 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 mPLUG-Owl2 have?

mPLUG-Owl2 has 7.1B parameters. "As depicted in Figure 2, our model, referred to as mPLUGOwl2, is composed of three main components: a fundamental vision encoder, a visual abstractor, and a language decoder. Specifically, we utilize ViT-L/14 as the vision encoder and LLaMA-2-7B [58] as the language decoder" ViT-L/14 has 123M parameters and Llama 2 7B has 7B 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 mPLUG-Owl2?

mPLUG-Owl2 was published by Alibaba, based in China, categorised as industry.

When was mPLUG-Owl2 released?

mPLUG-Owl2 was published in November 2023. 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 mPLUG-Owl2 used for?

mPLUG-Owl2 works in Vision, Language, Multimodal, and is recorded as handling visual question answering, Image captioning, Language modeling/generation. 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 mPLUG-Owl2?

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

Can I run mPLUG-Owl2 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 mPLUG-Owl2 is rarely worth using — the nearest miss we calculate is short by 1.4 GB. Every figure here assumes the whole model is on the card.

Source

Original publication

Record last updated 28 November 2025

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