Step-Video-T2V TPS calculator

Open weights StepFun 30B parameters February 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

132 cards that can run it

818 cards we hold specifications for

Smallest card that fits

RTX A4500

20 GB · IQ4_XS · 22.2 tok/s

Fastest card

B200

113 tok/s · 180 GB

Which GPUs can run Step-Video-T2V?

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

132 cards match

Calculating

					Needs	Quantisation	Fit
113 tok/s 68–181 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	32.8 GB	Q8_0	Comfortable
113 tok/s 68–181 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	32.8 GB	Q8_0	Comfortable
90.2 tok/s 54–144 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	32.8 GB	Q8_0	Comfortable
90.2 tok/s 54–144 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	32.8 GB	Q8_0	Comfortable
72.1 tok/s 43–115 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	32.8 GB	Q8_0	Comfortable
69.0 tok/s 41–110 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	32.8 GB	Q8_0	Comfortable
69.0 tok/s 41–110 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	32.8 GB	Q8_0	Comfortable
66.1 tok/s 40–106 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	32.8 GB	Q8_0	Comfortable
58.6 tok/s 35–94 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	32.8 GB	Q8_0	Comfortable
58.6 tok/s 35–94 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	32.8 GB	Q8_0	Comfortable
58.6 tok/s 35–94 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	32.8 GB	Q8_0	Comfortable
55.6 tok/s 33–89 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	32.8 GB	Q8_0	Comfortable
47.4 tok/s 28–76 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	32.8 GB	Q8_0	Comfortable
47.4 tok/s 28–76 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	32.8 GB	Q8_0	Comfortable
47.4 tok/s 28–76 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	32.8 GB	Q8_0	Comfortable
47.4 tok/s 28–76 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	32.8 GB	Q8_0	Comfortable
47.4 tok/s 28–76 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	32.8 GB	Q8_0	Comfortable
43.7 tok/s 26–70 · low confidence	GeForce RTX 5090 D V2 NVIDIA	24 GB	1,340 GB/s	Aug 2025	18.8 GB	Q4_K_M	Tight
39.8 tok/s 24–64 · low confidence	A30X NVIDIA	24 GB	1,220 GB/s	Apr 2021	18.8 GB	Q4_K_M	Tight
38.4 tok/s 23–61 · low confidence	DRIVE A100 PROD NVIDIA	32 GB	1,870 GB/s	May 2020	25.8 GB	Q6_K	Tight
38.4 tok/s 23–61 · low confidence	GRID A100A NVIDIA	32 GB	1,870 GB/s	May 2020	25.8 GB	Q6_K	Tight
36.7 tok/s 22–59 · low confidence	GeForce RTX 5090 NVIDIA	32 GB	1,790 GB/s	Jan 2025	25.8 GB	Q6_K	Tight
36.7 tok/s 22–59 · low confidence	GeForce RTX 5090 D NVIDIA	32 GB	1,790 GB/s	Jan 2025	25.8 GB	Q6_K	Tight
36.1 tok/s 22–58 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	32.8 GB	Q8_0	Comfortable
36.1 tok/s 22–58 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	32.8 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: StepFun
Organisation type: Industry
Country: China
Published: 24 February 2025
Authors: Guoqing Ma, Haoyang Huang, Kun Yan, Liangyu Chen, Nan Duan, Shengming Yin, Changyi Wan, Ranchen Ming, Xiaoniu Song, Xing Chen, Yu Zhou, Deshan Sun, Deyu Zhou, Jian Zhou, Kaijun Tan, Kang An, Mei Chen, Wei Ji, Qiling Wu, Wen Sun, Xin Han, Yanan Wei, Zheng Ge, Aojie Li, Bin Wang, Bizhu Huang, Bo Wang, Brian Li, Changxing Miao, Chen Xu, Chenfei Wu, Chenguang Yu, Dapeng Shi, Dingyuan Hu, Enle Liu, Gan…

What it does

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

Domain: Video
Task: Video generation, Text-to-video

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: 30B
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.

Training compute: 4.1 × 10²⁴ FLOP
How it was established: Hardware

The training run

What it physically took to train: which chips, how many, for how long, and what that drew from the wall.

Training hardware: NVIDIA H800 SXM5
Chips used: 5,000
Wall-clock time: 720 hours (30 days)
Hardware utilisation: MFU 32.0%
Power draw: 6.9 MW

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: stepfun-ai

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: Likely

Sources

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

Reference: Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Step-Video-T2V

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 Step-Video-T2V

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

RTX A4500

Memory needed

17.1 GB

Fastest

113 tok/s

With 30B parameters, Step-Video-T2V lands in the range a serious desktop card can handle once the weights are compressed. 132 of the cards we track can run it.

The least hardware that works is a RTX A4500. Its 20 GB is enough at IQ4_XS compression, giving roughly 22.2 tokens per second.

The quickest result comes from a B200 at around 113 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

Background

Step-Video-T2V was published by StepFun, in China, in February 2025. It comes out of industry.

It works in Video, and is recorded as doing video generation, Text-to-video.

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 stepfun-ai organisation on Hugging Face.

Reading the throughput figures

Across every card that can run it, the middle of the range is about 22.0 tokens per second, and 104 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.

Memory here is estimated from size rather than computed from the architecture, which is not recorded for this model — the numbers are indicative rather than exact.

How it was trained

Training it took roughly 4.1 × 10²⁴ FLOP of computation, on NVIDIA H800 SXM5 — a measure of what producing the model cost, not of how fast it answers.

Step by step

How to choose a GPU for Step-Video-T2V

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 Step-Video-T2V — around 17.1 GB at IQ4_XS. That figure, not the card's headline performance, is what decides whether it runs.
02

Match the context to your actual use

Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason Step-Video-T2V stops fitting a card that seemed fine.
03

Choose how far you will compress it

The quantisation column varies by card, because a bigger card holds a more accurate copy of Step-Video-T2V — IQ4_XS on the smallest card that fits. Set a floor to hold the comparison at one level.
04

Sort by speed

Sort by speed to see how cards rank for Step-Video-T2V. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 113 tok/s.
05

Read the fit column last

Tight means Step-Video-T2V 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

Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond Step-Video-T2V.

Answers

Step-Video-T2V — common questions

Can I run Step-Video-T2V 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 — the nearest miss we calculate is short by 4.4 GB. Our figures for Step-Video-T2V assume it is fully resident.

Would two GPUs run Step-Video-T2V faster?

A second card roughly doubles the memory available but not the generation rate. With 132 cards already able to run Step-Video-T2V alone, the case for pairing is weak.

Why does the quantisation differ between cards for Step-Video-T2V?

Because capacity varies, so does how hard Step-Video-T2V has to be squeezed — 5 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these Step-Video-T2V speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 68–181 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 Step-Video-T2V?

The smallest card in our catalogue that holds Step-Video-T2V is the RTX A4500, with 20 GB of memory. It runs the model at IQ4_XS using about 17.1 GB, and produces roughly 22.2 tokens per second. 132 cards in total can run it.

How fast is Step-Video-T2V on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 113 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 104 of the cards that can run Step-Video-T2V clear that.

How much VRAM does Step-Video-T2V need?

About 17.1 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 Step-Video-T2V on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q4_K_M, using about 18.8 GB and generating roughly 43.7 tokens per second — a tight fit.

Is Step-Video-T2V open source?

Its weights are published, so Step-Video-T2V 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 Step-Video-T2V have?

Step-Video-T2V has 30B parameters. 30B. 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 Step-Video-T2V?

Step-Video-T2V was published by StepFun, based in China, categorised as industry.

When was Step-Video-T2V released?

Step-Video-T2V was published in February 2025.

What is Step-Video-T2V used for?

Step-Video-T2V works in Video, and is recorded as handling video generation, Text-to-video. 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 Step-Video-T2V?

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

How much compute was used to train Step-Video-T2V?

Around 4.1 × 10²⁴ FLOP, on NVIDIA H800 SXM5. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.

Source

Original publication

Record last updated 28 November 2025

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