Qwen Image Edit TPS calculator

Open weights Alibaba 27B 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

241 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Xeon Phi 7120P

16 GB · Q3_K_M · 9.7 tok/s

Fastest card

B200

125 tok/s · 180 GB

Which GPUs can run Qwen Image Edit?

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

241 cards match

Calculating

					Needs	Quantisation	Fit
125 tok/s 75–201 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	29.6 GB	Q8_0	Comfortable
125 tok/s 75–201 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	29.6 GB	Q8_0	Comfortable
100 tok/s 60–160 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	29.6 GB	Q8_0	Comfortable
100 tok/s 60–160 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	29.6 GB	Q8_0	Comfortable
80.1 tok/s 48–128 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	29.6 GB	Q8_0	Comfortable
76.7 tok/s 46–123 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	29.6 GB	Q8_0	Comfortable
76.7 tok/s 46–123 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	29.6 GB	Q8_0	Comfortable
73.4 tok/s 44–117 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	29.6 GB	Q8_0	Comfortable
65.2 tok/s 39–104 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	29.6 GB	Q8_0	Comfortable
65.2 tok/s 39–104 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	29.6 GB	Q8_0	Comfortable
65.2 tok/s 39–104 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	29.6 GB	Q8_0	Comfortable
61.8 tok/s 37–99 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	29.6 GB	Q8_0	Comfortable
52.7 tok/s 32–84 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	29.6 GB	Q8_0	Comfortable
52.7 tok/s 32–84 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	29.6 GB	Q8_0	Comfortable
52.7 tok/s 32–84 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	29.6 GB	Q8_0	Comfortable
52.7 tok/s 32–84 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	29.6 GB	Q8_0	Comfortable
52.7 tok/s 32–84 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	29.6 GB	Q8_0	Comfortable
47.8 tok/s 29–77 · low confidence	Tesla V100 SXM2 16 GB NVIDIA	16 GB	1,130 GB/s	Nov 2019	13.9 GB	Q3_K_M	Tight
42.6 tok/s 26–68 · low confidence	DRIVE A100 PROD NVIDIA	32 GB	1,870 GB/s	May 2020	23.3 GB	Q6_K	Comfortable
42.6 tok/s 26–68 · low confidence	GRID A100A NVIDIA	32 GB	1,870 GB/s	May 2020	23.3 GB	Q6_K	Comfortable
40.8 tok/s 24–65 · low confidence	GeForce RTX 5090 NVIDIA	32 GB	1,790 GB/s	Jan 2025	23.3 GB	Q6_K	Comfortable
40.8 tok/s 24–65 · low confidence	GeForce RTX 5090 D NVIDIA	32 GB	1,790 GB/s	Jan 2025	23.3 GB	Q6_K	Comfortable
40.6 tok/s 24–65 · low confidence	GeForce RTX 5080 NVIDIA	16 GB	960 GB/s	Jan 2025	13.9 GB	Q3_K_M	Tight
40.1 tok/s 24–64 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	29.6 GB	Q8_0	Comfortable
40.1 tok/s 24–64 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	29.6 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: 18 August 2025

What it does

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

Domain: Image generation
Task: Image-to-image
Base model: Qwen Image

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: 27B
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: 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: Qwen-Image-Edit: Image Editing with Higher Quality and Efficiency
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Qwen Image Edit

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 Qwen Image Edit

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

Xeon Phi 7120P

Memory needed

13.9 GB

Fastest

125 tok/s

With 27B parameters, Qwen Image Edit lands in the range a serious desktop card can handle once the weights are compressed. 241 of the cards we track can run it.

At the low end, a Xeon Phi 7120P handles it — 16 GB, at Q3_K_M, for about 9.7 tokens per second.

The quickest result comes from a B200 at around 125 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

About this model

Qwen Image Edit was published by Alibaba, in China, in August 2025. It comes out of industry.

It works in Image generation, and is recorded as doing image-to-image.

Its starting point was Qwen Image — 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 Qwen organisation on Hugging Face.

How fast it runs, and why

Across every card that can run it, the middle of the range is about 19.0 tokens per second, and 196 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 Qwen Image Edit

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 Qwen Image Edit — around 13.9 GB at Q3_K_M. That figure, not the card's headline performance, is what decides whether it runs.
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 Qwen Image Edit can slip off a card that handles short questions easily.
03

Decide how much compression you will accept

Each card runs the least-compressed copy it can hold — Q3_K_M on the smallest card that fits. Setting a floor drops the cards that only manage Qwen Image Edit by squeezing it further than you would want.
04

Rank by throughput rather than spec sheet

Ranking by tokens per second for Qwen Image Edit follows memory bandwidth, not core counts, which is why the B200 tops it at 125 tok/s.
05

Read the fit column last

Tight means Qwen Image Edit 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

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

Answers

Qwen Image Edit — common questions

Can I run Qwen Image Edit on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q3_K_M, using about 13.9 GB and generating roughly 47.8 tokens per second — a tight fit.

Can I run Qwen Image Edit on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q5_K_M, using about 20.2 GB and generating roughly 37.5 tokens per second — a tight fit.

Is Qwen Image Edit open source?

Its weights are published, so Qwen Image Edit 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 Qwen Image Edit have?

Qwen Image Edit has 27B parameters. "Built upon our 20B Qwen-Image model" Qwen Image has 27B parameters though. 20B is just the size of the main module (MMDiT). 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 Qwen Image Edit?

Qwen Image Edit was published by Alibaba, based in China, categorised as industry.

When was Qwen Image Edit released?

Qwen Image Edit was published in August 2025.

What is Qwen Image Edit used for?

Qwen Image Edit works in Image generation, and is recorded as handling image-to-image. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download Qwen Image Edit?

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 Qwen Image Edit if it does not fit in my GPU?

It can be split between the card and system memory, but Qwen Image Edit generates painfully slowly that way — the nearest miss we calculate is short by 6.2 GB. Nothing on this page assumes offloading.

Would two GPUs run Qwen Image Edit faster?

Two cards buy memory rather than speed. That matters for Qwen Image Edit only if one card cannot hold it — 241 can, so a second adds little.

Why does the quantisation differ between cards for Qwen Image Edit?

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

How accurate are these Qwen Image Edit 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 75–201 tok/s on the B200 rather than a single number.

What GPU do I need to run Qwen Image Edit?

The smallest card in our catalogue that holds Qwen Image Edit is the Xeon Phi 7120P, with 16 GB of memory. It runs the model at Q3_K_M using about 13.9 GB, and produces roughly 9.7 tokens per second. 241 cards in total can run it.

How fast is Qwen Image Edit on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 125 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 196 of the cards that can run Qwen Image Edit clear that.

How much VRAM does Qwen Image Edit need?

About 13.9 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.

Source

Original publication

Record last updated 28 November 2025

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