Stable Diffusion 3.5 Medium TPS calculator

Open weights Stability AI 2.5B parameters October 2024

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 · Q8_0 · 14.8 tok/s

Fastest card

B200

1,355 tok/s · 180 GB

Which GPUs can run Stable Diffusion 3.5 Medium?

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

818 cards match

Calculating

					Needs	Quantisation	Fit
1,355 tok/s 813–2,168 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	3.4 GB	Q8_0	Comfortable
1,355 tok/s 813–2,168 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	3.4 GB	Q8_0	Comfortable
1,082 tok/s 649–1,732 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	3.4 GB	Q8_0	Comfortable
1,082 tok/s 649–1,732 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	3.4 GB	Q8_0	Comfortable
866 tok/s 519–1,385 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	3.4 GB	Q8_0	Comfortable
828 tok/s 497–1,325 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	3.4 GB	Q8_0	Comfortable
828 tok/s 497–1,325 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	3.4 GB	Q8_0	Comfortable
793 tok/s 476–1,269 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	3.4 GB	Q8_0	Comfortable
704 tok/s 422–1,126 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	3.4 GB	Q8_0	Comfortable
704 tok/s 422–1,126 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	3.4 GB	Q8_0	Comfortable
704 tok/s 422–1,126 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	3.4 GB	Q8_0	Comfortable
667 tok/s 400–1,068 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	3.4 GB	Q8_0	Comfortable
569 tok/s 342–911 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	3.4 GB	Q8_0	Comfortable
569 tok/s 342–911 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	3.4 GB	Q8_0	Comfortable
569 tok/s 342–911 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	3.4 GB	Q8_0	Comfortable
569 tok/s 342–911 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	3.4 GB	Q8_0	Comfortable
569 tok/s 342–911 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	3.4 GB	Q8_0	Comfortable
433 tok/s 260–693 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	3.4 GB	Q8_0	Comfortable
433 tok/s 260–693 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	3.4 GB	Q8_0	Comfortable
361 tok/s 217–578 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	3.4 GB	Q8_0	Comfortable
353 tok/s 212–566 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	3.4 GB	Q8_0	Comfortable
346 tok/s 207–553 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	3.4 GB	Q8_0	Comfortable
346 tok/s 207–553 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	3.4 GB	Q8_0	Comfortable
346 tok/s 207–553 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	3.4 GB	Q8_0	Comfortable
346 tok/s 207–553 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	3.4 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: Stability AI
Organisation type: Industry
Country: United Kingdom of Great Britain and Northern Ireland
Published: 29 October 2024

What it does

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

Domain: Image generation
Task: Image generation, Text-to-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: 2.5B
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 (restricted use)
Training code: Unreleased
Hugging Face: stabilityai

How it is classified

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

Likely above 10²³ FLOP: Yes
Record confidence: Confident

Sources

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

Reference: Introducing Stable Diffusion 3.5
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Stable Diffusion 3.5 Medium

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 Stable Diffusion 3.5 Medium

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 it takes to run this model

Minimum card

Tesla C1080

Memory needed

3.4 GB

Fastest

1,355 tok/s

Stable Diffusion 3.5 Medium is small enough at 2.5B parameters that hardware is rarely the obstacle — 818 of the cards we track can run it, including cards several years old.

The smallest card that holds it is the Tesla C1080 with 4 GB, running it at Q8_0 and producing around 14.8 tokens per second.

Top of the range is the B200, at roughly 1,355 tokens per second thanks to 8,000 GB/s of bandwidth.

What this model is

Stable Diffusion 3.5 Medium was published by Stability AI, in United Kingdom of Great Britain and Northern Ireland, in October 2024. It comes out of industry.

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

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 stabilityai organisation on Hugging Face.

What decides the speed

The median result is around 38.1 tokens per second; 783 cards produce text faster than most people read it.

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.

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 Stable Diffusion 3.5 Medium

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 Stable Diffusion 3.5 Medium — around 3.4 GB at Q8_0. That figure, not the card's headline performance, is what decides whether it runs.
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 Stable Diffusion 3.5 Medium.
03

Set a quality floor

Compression is what makes Stable Diffusion 3.5 Medium fit smaller cards, at some cost in accuracy — Q8_0 on the smallest card that fits. A minimum quality removes the ones that go too far.
04

Rank by throughput rather than spec sheet

The speed ordering for Stable Diffusion 3.5 Medium is effectively an ordering by memory bandwidth, which is why the B200 tops it at 1,355 tok/s.
05

Look at the headroom, not just the fit

Tight means Stable Diffusion 3.5 Medium 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 Stable Diffusion 3.5 Medium alone — a card is usually bought for more than one model.

Answers

Stable Diffusion 3.5 Medium — common questions

How many parameters does Stable Diffusion 3.5 Medium have?

Stable Diffusion 3.5 Medium has 2.5B parameters. 2.5B. 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 Stable Diffusion 3.5 Medium?

Stable Diffusion 3.5 Medium was published by Stability AI, based in United Kingdom of Great Britain and Northern Ireland, categorised as industry.

When was Stable Diffusion 3.5 Medium released?

Stable Diffusion 3.5 Medium was published in October 2024. 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 Stable Diffusion 3.5 Medium used for?

Stable Diffusion 3.5 Medium works in Image generation, and is recorded as handling image generation, Text-to-image. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Where can I download Stable Diffusion 3.5 Medium?

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

Can I run Stable Diffusion 3.5 Medium 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 Stable Diffusion 3.5 Medium assume it is fully resident.

Would two GPUs run Stable Diffusion 3.5 Medium faster?

A second card roughly doubles the memory available but not the generation rate. With 818 cards already able to run Stable Diffusion 3.5 Medium alone, the case for pairing is weak.

Why does the quantisation differ between cards for Stable Diffusion 3.5 Medium?

Because capacity varies, so does how hard Stable Diffusion 3.5 Medium has to be squeezed — 1 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these Stable Diffusion 3.5 Medium speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 813–2,168 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 Stable Diffusion 3.5 Medium?

The smallest card in our catalogue that holds Stable Diffusion 3.5 Medium is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 3.4 GB, and produces roughly 14.8 tokens per second. 818 cards in total can run it.

How fast is Stable Diffusion 3.5 Medium on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 1,355 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 783 of the cards that can run Stable Diffusion 3.5 Medium clear that.

How much VRAM does Stable Diffusion 3.5 Medium need?

About 3.4 GB at Q8_0 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 Stable Diffusion 3.5 Medium on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 3.4 GB and generating roughly 252 tokens per second — a comfortable fit.

Can I run Stable Diffusion 3.5 Medium on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 3.4 GB and generating roughly 155 tokens per second — a comfortable fit.

Can I run Stable Diffusion 3.5 Medium on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 3.4 GB and generating roughly 191 tokens per second — a comfortable fit.

Can I run Stable Diffusion 3.5 Medium on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 3.4 GB and generating roughly 227 tokens per second — a comfortable fit.

Is Stable Diffusion 3.5 Medium open source?

Its weights are published, so Stable Diffusion 3.5 Medium 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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