Stable Cascade TPS calculator

Open weights Stability AI 5.1B parameters February 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 · IQ4_XS · 17.7 tok/s

Fastest card

B200

662 tok/s · 180 GB

Which GPUs can run Stable Cascade?

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
662 tok/s 397–1,059 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	6.2 GB	Q8_0	Comfortable
662 tok/s 397–1,059 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	6.2 GB	Q8_0	Comfortable
528 tok/s 317–846 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	6.2 GB	Q8_0	Comfortable
528 tok/s 317–846 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	6.2 GB	Q8_0	Comfortable
423 tok/s 254–676 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	6.2 GB	Q8_0	Comfortable
405 tok/s 243–647 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	6.2 GB	Q8_0	Comfortable
405 tok/s 243–647 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	6.2 GB	Q8_0	Comfortable
387 tok/s 232–619 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	6.2 GB	Q8_0	Comfortable
344 tok/s 206–550 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	6.2 GB	Q8_0	Comfortable
344 tok/s 206–550 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	6.2 GB	Q8_0	Comfortable
344 tok/s 206–550 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	6.2 GB	Q8_0	Comfortable
326 tok/s 196–521 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	6.2 GB	Q8_0	Comfortable
278 tok/s 167–445 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	6.2 GB	Q8_0	Comfortable
278 tok/s 167–445 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	6.2 GB	Q8_0	Comfortable
278 tok/s 167–445 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	6.2 GB	Q8_0	Comfortable
278 tok/s 167–445 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	6.2 GB	Q8_0	Comfortable
278 tok/s 167–445 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	6.2 GB	Q8_0	Comfortable
212 tok/s 127–339 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	6.2 GB	Q8_0	Comfortable
212 tok/s 127–339 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	6.2 GB	Q8_0	Comfortable
176 tok/s 106–282 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	6.2 GB	Q8_0	Comfortable
173 tok/s 104–276 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	6.2 GB	Q8_0	Comfortable
169 tok/s 101–270 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	6.2 GB	Q8_0	Comfortable
169 tok/s 101–270 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	6.2 GB	Q8_0	Comfortable
169 tok/s 101–270 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	6.2 GB	Q8_0	Comfortable
169 tok/s 101–270 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	6.2 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: 12 February 2024

What it does

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

Domain: Image generation
Task: Text-to-image, Image generation

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: 5.1B
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 (non-commercial)
Training code: Open source

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: Introducing Stable Cascade
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Stable Cascade

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 Cascade

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

3.5 GB

Fastest

662 tok/s

Stable Cascade is small enough at 5.1B 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 IQ4_XS and producing around 17.7 tokens per second.

At the other end, a B200 generates roughly 662 tokens per second on it, on the strength of 8,000 GB/s of memory bandwidth.

What this model is

Stable Cascade was published by Stability AI, in United Kingdom of Great Britain and Northern Ireland, in February 2024. industry is the category the publisher falls under.

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

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.

What decides the speed

Half the cards that hold it manage more than 26.4 tokens per second, and 770 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.

Step by step

How to choose a GPU for Stable Cascade

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

Look at what Stable Cascade actually needs — around 3.5 GB at IQ4_XS. No amount of processing power compensates for a card that cannot hold it.
02

Decide how long your conversations run

Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason Stable Cascade stops fitting a card that seemed fine.
03

Choose how far you will compress it

Compression is what makes Stable Cascade fit smaller cards, at some cost in accuracy — IQ4_XS on the smallest card that fits. A minimum quality removes the ones that go too far.
04

Rank by throughput rather than spec sheet

Ranking by tokens per second for Stable Cascade follows memory bandwidth, not core counts, which is why the B200 tops it at 662 tok/s.
05

Check the fit verdict before buying

A tight fit runs Stable Cascade but leaves nothing spare for a longer conversation; comfortable has headroom. If you expect to grow the context, buy for comfortable.
06

See what else that card runs

Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond Stable Cascade.

Answers

Stable Cascade — common questions

What GPU do I need to run Stable Cascade?

The smallest card in our catalogue that holds Stable Cascade is the Tesla C1080, with 4 GB of memory. It runs the model at IQ4_XS using about 3.5 GB, and produces roughly 17.7 tokens per second. 818 cards in total can run it.

How fast is Stable Cascade on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 662 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 770 of the cards that can run Stable Cascade clear that.

How much VRAM does Stable Cascade need?

About 3.5 GB at IQ4_XS 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 Cascade on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 6.2 GB and generating roughly 123 tokens per second — a tight fit.

Can I run Stable Cascade on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 6.2 GB and generating roughly 75.5 tokens per second — a comfortable fit.

Can I run Stable Cascade on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 6.2 GB and generating roughly 93.5 tokens per second — a comfortable fit.

Can I run Stable Cascade on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 6.2 GB and generating roughly 111 tokens per second — a comfortable fit.

Is Stable Cascade open source?

Its weights are published, so Stable Cascade 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 Stable Cascade have?

Stable Cascade has 5.1B parameters. "we are providing two checkpoints for Stage C, two for Stage B and one for Stage A. Stage C comes with a 1 billion and 3.6 billion parameter version, but we highly recommend using the 3.6 billion version, as most work was put into its finetuning. The two versions for Stage B amount to 700 million and 1.5 billion parameters. Both achieve great results, however the 1.5 billion excels at reconstructing small and fine details. Therefore, you will achieve the best results if you use the larger variant of each. Lastly, Stage A contains 20 million parameters and is fixed due to its small size." 3.6B + 1.5B + 20M = 5.12B. 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 Cascade?

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

When was Stable Cascade released?

Stable Cascade was published in February 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 Cascade used for?

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

Where can I download Stable Cascade?

The weights for Stable Cascade are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.

Can I run Stable Cascade 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 Cascade assume it is fully resident.

Would two GPUs run Stable Cascade faster?

Two cards buy memory rather than speed. That matters for Stable Cascade only if one card cannot hold it — 818 can, so a second adds little.

Why does the quantisation differ between cards for Stable Cascade?

Each card is shown running the least-compressed copy it can hold, and Stable Cascade appears at 4 different compression levels across the cards that fit it. Bigger cards get the more accurate version.

How accurate are these Stable Cascade 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 397–1,059 tok/s on the B200 rather than a single number.

Source

Original publication

Record last updated 28 November 2025

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