Step-Audio-Chat 130B TPS calculator

Open weights StepFun 130B 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

38 cards that can run it

818 cards we hold specifications for

Smallest card that fits

RTX PRO 5000 72 GB Blackwell

72 GB · Q3_K_M · 11.8 tok/s

Fastest card

H100 NVL 94 GB

29.6 tok/s · 94 GB

Which GPUs can run Step-Audio-Chat 130B?

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

38 cards match

Calculating

					Needs	Quantisation	Fit
29.6 tok/s 18–47 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	79.3 GB	Q4_K_M	Tight
26.9 tok/s 16–43 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	71.8 GB	IQ4_XS	Tight
26.9 tok/s 16–43 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	71.8 GB	IQ4_XS	Tight
26.1 tok/s 16–42 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	139.9 GB	Q8_0	Tight
26.1 tok/s 16–42 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	139.9 GB	Q8_0	Comfortable
25.3 tok/s 15–40 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	79.3 GB	Q4_K_M	Tight
25.3 tok/s 15–40 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	79.3 GB	Q4_K_M	Tight
25.3 tok/s 15–40 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	79.3 GB	Q4_K_M	Tight
24.2 tok/s 15–39 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	109.6 GB	Q6_K	Tight
23.2 tok/s 14–37 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	109.6 GB	Q6_K	Tight
23.2 tok/s 14–37 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	109.6 GB	Q6_K	Tight
20.8 tok/s 12–33 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	139.9 GB	Q8_0	Comfortable
20.8 tok/s 12–33 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	139.9 GB	Q8_0	Comfortable
19.7 tok/s 12–31 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	109.6 GB	Q6_K	Tight
16.3 tok/s 10–26 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	71.8 GB	IQ4_XS	Tight
16.3 tok/s 10–26 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	71.8 GB	IQ4_XS	Tight
16.3 tok/s 10–26 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	71.8 GB	IQ4_XS	Tight
16.3 tok/s 10–26 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	71.8 GB	IQ4_XS	Tight
16.3 tok/s 10–26 · low confidence	H100 PCIe 80 GB NVIDIA	80 GB	2,040 GB/s	Oct 2022	71.8 GB	IQ4_XS	Tight
16.3 tok/s 10–26 · low confidence	H800 PCIe 80 GB NVIDIA	80 GB	2,040 GB/s	Mar 2023	71.8 GB	IQ4_XS	Tight
15.5 tok/s 9–25 · low confidence	A100 PCIe 80 GB NVIDIA	80 GB	1,940 GB/s	Jun 2021	71.8 GB	IQ4_XS	Tight
15.5 tok/s 9–25 · low confidence	A800 PCIe 80 GB NVIDIA	80 GB	1,940 GB/s	Nov 2022	71.8 GB	IQ4_XS	Tight
15.3 tok/s 9–24 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	139.9 GB	Q8_0	Comfortable
13.5 tok/s 8–22 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	139.9 GB	Q8_0	Comfortable
13.5 tok/s 8–22 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	139.9 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: StepFun
Organisation type: Industry
Country: China
Published: 18 February 2025
Authors: Ailin Huang, Boyong Wu, Bruce Wang, Chao Yan, Chen Hu, Chengli Feng, Fei Tian, Feiyu Shen, Jingbei Li, Mingrui Chen, Peng Liu, Ruihang Miao, Wang You, Xi Chen, Xuerui Yang, Yechang Huang, Yuxiang Zhang, Zheng Gong, Zixin Zhang, Hongyu Zhou, Jianjian Sun, Brian Li, Chengting Feng, Changyi Wan, Hanpeng Hu, Jianchang Wu, Jiangjie Zhen, Ranchen Ming, Song Yuan, Xuelin Zhang, Yu Zhou, Bingxin Li, Buyun…

What it does

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

Domain: Speech
Task: Speech synthesis, Speech recognition (ASR), Speech-to-text, Text-to-speech (TTS), Audio question answering, Audio generation, Speech-to-speech
Base model: Step-1

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: 130B
Training data: 1,668,000,000,000 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: 1.9 × 10²⁴ FLOP
How it was established: Operation counting

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.

Record confidence: Confident

Sources

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

Reference: Step-Audio: Unified Understanding and Generation in Intelligent Speech Interaction
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Step-Audio-Chat 130B

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-Audio-Chat 130B

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 PRO 5000 72 GB Blackwell

Memory needed

64.2 GB

Fastest

29.6 tok/s

Step-Audio-Chat 130B reaches a parameter count of 130B. That puts it above consumer hardware, into the range where a card is bought for this purpose rather than repurposed for it. The number of cards we track that can hold it: 38.

The least hardware that works is RTX PRO 5000 72 GB Blackwell, with a memory capacity of 72 GB, running it at a compression of Q3_K_M and producing around 11.8 tokens per second.

The quickest result comes from H100 NVL 94 GB, generating roughly 29.6 tokens per second on the strength of a memory bandwidth of 3,940 GB/s.

Where it came from

Step-Audio-Chat 130B was published by StepFun, in the country recorded as China, during February 2025. It comes out of an organisation categorised as industry.

It works in the domain of Speech, and is recorded as performing the task of speech synthesis, Speech recognition (ASR), Speech-to-text, Text-to-speech (TTS), Audio question answering, Audio generation, Speech-to-speech.

Rather than being trained from scratch, it is derived from Step-1. That is why it shares the base model's general shape and size.

Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded. On Hugging Face it is published under the organisation stepfun-ai.

Understanding the speeds

Half the cards that hold it manage more than 16.3 tokens per second. Producing text faster than most people read it: 35 of them.

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.

Training and provenance

Producing it required arithmetic totalling around 1.9 × 10²⁴ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.

Training consumed a corpus of around 1,668,000,000,000 tokens of text.

Step by step

How to choose a GPU for Step-Audio-Chat 130B

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

Every card here has been checked against Step-Audio-Chat 130B, needing around 64.2 GB at a compression of Q3_K_M. No amount of processing power compensates for a card that cannot hold it.
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 a card that seemed fine stops fitting Step-Audio-Chat 130B.
03

Choose how far you will compress it

The quantisation column varies by card, because a bigger card holds a more accurate copy, reaching a compression of Q3_K_M on the smallest card that fits. Setting a minimum quality drops the cards that only manage it by squeezing further than you would want, and holds the comparison at one level.
04

Rank by throughput rather than spec sheet

Sort by speed to see how cards rank for Step-Audio-Chat 130B. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is H100 NVL 94 GB, at 29.6 tok/s.
05

Read the fit column last

A tight fit runs, but leaves nothing spare for a longer conversation, in the case of Step-Audio-Chat 130B. Comfortable means you can grow the context later. That difference matters more than a few tokens per second, so buy for comfortable if you expect to.
06

Open the card you have settled on

Each card page repeats this sweep for every model we hold, answering what else the hardware is good for beyond Step-Audio-Chat 130B.

Answers

Step-Audio-Chat 130B — common questions

Step-Audio-Chat 130B— why does the quantisation differ between cards?

A larger card holds a more accurate copy. The number of compression levels used across the cards that run it: 5. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

Step-Audio-Chat 130B— how accurate are these speed estimates?

Every figure is derived from memory bandwidth and model size, not benchmarked, which is why each is published as a range rather than a single number. One example: 18–47 tok/s on H100 NVL 94 GB. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

Step-Audio-Chat 130B— what GPU do I need to run it?

The smallest card in our catalogue that holds it is RTX PRO 5000 72 GB Blackwell, with a memory capacity of 72 GB. It runs the model at a compression of Q3_K_M using about 64.2 GB, and produces roughly 11.8 tokens per second. The number of cards able to run it in total: 38.

Step-Audio-Chat 130B— how fast is it on a GPU?

It depends on the card. The quickest we calculate is H100 NVL 94 GB, at about 29.6 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and the number of cards clearing that: 35.

Step-Audio-Chat 130B— how much VRAM does it need?

It needs about 64.2 GB at a compression of Q3_K_M, 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.

Step-Audio-Chat 130B— is it open source?

Its weights are published, so it 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.

Step-Audio-Chat 130B— how many parameters does it have?

It has a parameter count of 130B. 130B. 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.

Step-Audio-Chat 130B— who created it?

It was published by StepFun, based in China, an organisation categorised as industry.

Step-Audio-Chat 130B— when was it released?

It was published in February 2025.

Step-Audio-Chat 130B— what is it used for?

It works in the domain of Speech, and is recorded as handling the task of speech synthesis, Speech recognition (ASR), Speech-to-text, Text-to-speech (TTS), Audio question answering, Audio generation, Speech-to-speech. 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.

Step-Audio-Chat 130B— where can I download it?

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

Step-Audio-Chat 130B— how much compute was used to train it?

Training consumed around 1.9 × 10²⁴ FLOP. 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.

Step-Audio-Chat 130B— can I run it if it does not fit in my GPU?

It can be split between the card and system memory, but it generates painfully slowly that way. The nearest miss we calculate falls short by 21.7 GB. Every figure here assumes the whole model is resident on the card.

Step-Audio-Chat 130B— would two GPUs run it faster?

A second card roughly doubles the memory available but not the generation rate. The number already able to run it alone: 38. So a second card is rarely the answer here.

Source

Original publication

Record last updated 28 November 2025

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