Qwen2.5-Omni 3B TPS calculator

Open weights Alibaba 3B 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

818 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Tesla C1080

4 GB · Q6_K · 17.9 tok/s

Fastest card

B200

1,129 tok/s · 180 GB

Which GPUs can run Qwen2.5-Omni 3B?

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

818 cards match

Calculating

					Needs	Quantisation	Fit
1,129 tok/s 678–1,807 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	3.9 GB	Q8_0	Comfortable
1,129 tok/s 678–1,807 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	3.9 GB	Q8_0	Comfortable
902 tok/s 541–1,443 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	3.9 GB	Q8_0	Comfortable
902 tok/s 541–1,443 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	3.9 GB	Q8_0	Comfortable
721 tok/s 433–1,154 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	3.9 GB	Q8_0	Comfortable
690 tok/s 414–1,105 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	3.9 GB	Q8_0	Comfortable
690 tok/s 414–1,105 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	3.9 GB	Q8_0	Comfortable
661 tok/s 396–1,057 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	3.9 GB	Q8_0	Comfortable
586 tok/s 352–938 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	3.9 GB	Q8_0	Comfortable
586 tok/s 352–938 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	3.9 GB	Q8_0	Comfortable
586 tok/s 352–938 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	3.9 GB	Q8_0	Comfortable
556 tok/s 334–890 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	3.9 GB	Q8_0	Comfortable
474 tok/s 285–759 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	3.9 GB	Q8_0	Comfortable
474 tok/s 285–759 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	3.9 GB	Q8_0	Comfortable
474 tok/s 285–759 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	3.9 GB	Q8_0	Comfortable
474 tok/s 285–759 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	3.9 GB	Q8_0	Comfortable
474 tok/s 285–759 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	3.9 GB	Q8_0	Comfortable
361 tok/s 217–578 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	3.9 GB	Q8_0	Comfortable
361 tok/s 217–578 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	3.9 GB	Q8_0	Comfortable
301 tok/s 181–482 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	3.9 GB	Q8_0	Comfortable
295 tok/s 177–471 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	3.9 GB	Q8_0	Comfortable
288 tok/s 173–461 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	3.9 GB	Q8_0	Comfortable
288 tok/s 173–461 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	3.9 GB	Q8_0	Comfortable
288 tok/s 173–461 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	3.9 GB	Q8_0	Comfortable
288 tok/s 173–461 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	3.9 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: 26 March 2025
Authors: Jin Xu, Zhifang Guo, Jinzheng He, Hangrui Hu, Ting He, Shuai Bai, Keqin Chen, Jialin Wang, Yang Fan, Kai Dang, Bin Zhang, Xiong Wang, Yunfei Chu, Junyang Lin

What it does

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

Domain: Multimodal, Language, Vision, Video, Audio, Speech
Task: Language modeling/generation, Question answering, Visual question answering, Speech synthesis, Speech recognition (ASR), Speech-to-speech, Audio question answering, Speech-to-text, Text-to-speech (TTS), Video description, Image captioning
Base model: Qwen2.5-3B

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: 3B
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
Fine-tuning compute: 2.2 × 10²² 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 (non-commercial)
Training code: Unreleased
Hugging Face: Qwen

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: Qwen2.5-Omni Technical Report
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Qwen2.5-Omni 3B

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 Qwen2.5-Omni 3B

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 you need to run it

Minimum card

Tesla C1080

Memory needed

3.2 GB

Fastest

1,129 tok/s

Qwen2.5-Omni 3B is small enough at 3B parameters that hardware is rarely the obstacle — 818 of the cards we track can run it, including cards several years old.

The least hardware that works is a Tesla C1080. Its 4 GB is enough at Q6_K compression, giving roughly 17.9 tokens per second.

The quickest result comes from a B200 at around 1,129 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

Where it came from

Qwen2.5-Omni 3B was published by Alibaba, in China, in March 2025. It comes out of industry.

It works in Multimodal, Language, Vision, Video, Audio, Speech, and is recorded as doing language modeling/generation, Question answering, Visual question answering, Speech synthesis, Speech recognition (ASR), Speech-to-speech, Audio question answering, Speech-to-text, Text-to-speech (TTS), Video description, Image captioning.

It builds on Qwen2.5-3B, which is why it shares that model's general shape and size.

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 Qwen organisation on Hugging Face.

Understanding the speeds

Across every card that can run it, the middle of the range is about 35.8 tokens per second, and 780 of them clear the ten tokens per second that roughly matches reading speed.

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 Qwen2.5-Omni 3B

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

Look at what Qwen2.5-Omni 3B actually needs — around 3.2 GB at Q6_K. No amount of processing power compensates for a card that cannot hold it.
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 Qwen2.5-Omni 3B.
03

Set a quality floor

Compression is what makes Qwen2.5-Omni 3B fit smaller cards, at some cost in accuracy — Q6_K on the smallest card that fits. A minimum quality removes the ones that go too far.
04

Compare tokens per second, not specifications

Sort by speed to see how cards rank for Qwen2.5-Omni 3B. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 1,129 tok/s.
05

Read the fit column last

The fit column separates cards that just manage Qwen2.5-Omni 3B from those with room to spare. Buy for the second if the context might grow.
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 Qwen2.5-Omni 3B alone — a card is usually bought for more than one model.

Answers

Qwen2.5-Omni 3B — common questions

Is Qwen2.5-Omni 3B open source?

Its weights are published, so Qwen2.5-Omni 3B 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 Qwen2.5-Omni 3B have?

Qwen2.5-Omni 3B has 3B parameters. 3B. 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 Qwen2.5-Omni 3B?

Qwen2.5-Omni 3B was published by Alibaba, based in China, categorised as industry.

When was Qwen2.5-Omni 3B released?

Qwen2.5-Omni 3B was published in March 2025.

What is Qwen2.5-Omni 3B used for?

Qwen2.5-Omni 3B works in Multimodal, Language, Vision, Video, Audio, Speech, and is recorded as handling language modeling/generation, Question answering, Visual question answering, Speech synthesis, Speech recognition (ASR), Speech-to-speech, Audio question answering, Speech-to-text, Text-to-speech (TTS), Video description, Image captioning. 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 Qwen2.5-Omni 3B?

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

Can I run Qwen2.5-Omni 3B if it does not fit in my GPU?

Only by offloading, which is usually a false economy: the part in system memory drags the whole thing down. Our figures for Qwen2.5-Omni 3B assume it is fully resident.

Would two GPUs run Qwen2.5-Omni 3B faster?

Two cards buy memory rather than speed. That matters for Qwen2.5-Omni 3B only if one card cannot hold it — 818 can, so a second adds little.

Why does the quantisation differ between cards for Qwen2.5-Omni 3B?

Each card is shown running the least-compressed copy it can hold, and Qwen2.5-Omni 3B appears at 2 different compression levels across the cards that fit it. Bigger cards get the more accurate version.

How accurate are these Qwen2.5-Omni 3B 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 678–1,807 tok/s on the B200 rather than a single number.

What GPU do I need to run Qwen2.5-Omni 3B?

The smallest card in our catalogue that holds Qwen2.5-Omni 3B is the Tesla C1080, with 4 GB of memory. It runs the model at Q6_K using about 3.2 GB, and produces roughly 17.9 tokens per second. 818 cards in total can run it.

How fast is Qwen2.5-Omni 3B on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 1,129 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 780 of the cards that can run Qwen2.5-Omni 3B clear that.

How much VRAM does Qwen2.5-Omni 3B need?

About 3.2 GB at Q6_K 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 Qwen2.5-Omni 3B on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 3.9 GB and generating roughly 210 tokens per second — a comfortable fit.

Can I run Qwen2.5-Omni 3B on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 3.9 GB and generating roughly 129 tokens per second — a comfortable fit.

Can I run Qwen2.5-Omni 3B on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 3.9 GB and generating roughly 160 tokens per second — a comfortable fit.

Can I run Qwen2.5-Omni 3B on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 3.9 GB and generating roughly 189 tokens per second — a comfortable fit.

Source

Original publication

Record last updated 28 November 2025

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