Zonos-v0.1 TPS calculator

Open weights Zyphra 1.6B 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

818 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Tesla C1080

4 GB · Q8_0 · 23.0 tok/s

Fastest card

B200

2,118 tok/s · 180 GB

Which GPUs can run Zonos-v0.1?

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
2,118 tok/s 1,271–3,388 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	2.4 GB	Q8_0	Comfortable
2,118 tok/s 1,271–3,388 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	2.4 GB	Q8_0	Comfortable
1,691 tok/s 1,015–2,706 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	2.4 GB	Q8_0	Comfortable
1,691 tok/s 1,015–2,706 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	2.4 GB	Q8_0	Comfortable
1,352 tok/s 811–2,164 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	2.4 GB	Q8_0	Comfortable
1,294 tok/s 777–2,071 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	2.4 GB	Q8_0	Comfortable
1,294 tok/s 777–2,071 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	2.4 GB	Q8_0	Comfortable
1,239 tok/s 743–1,982 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	2.4 GB	Q8_0	Comfortable
1,099 tok/s 660–1,759 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	2.4 GB	Q8_0	Comfortable
1,099 tok/s 660–1,759 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	2.4 GB	Q8_0	Comfortable
1,099 tok/s 660–1,759 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	2.4 GB	Q8_0	Comfortable
1,043 tok/s 626–1,669 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	2.4 GB	Q8_0	Comfortable
889 tok/s 534–1,423 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	2.4 GB	Q8_0	Comfortable
889 tok/s 534–1,423 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	2.4 GB	Q8_0	Comfortable
889 tok/s 534–1,423 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	2.4 GB	Q8_0	Comfortable
889 tok/s 534–1,423 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	2.4 GB	Q8_0	Comfortable
889 tok/s 534–1,423 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	2.4 GB	Q8_0	Comfortable
677 tok/s 406–1,084 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	2.4 GB	Q8_0	Comfortable
677 tok/s 406–1,084 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	2.4 GB	Q8_0	Comfortable
564 tok/s 339–903 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	2.4 GB	Q8_0	Comfortable
552 tok/s 331–884 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	2.4 GB	Q8_0	Comfortable
540 tok/s 324–864 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	2.4 GB	Q8_0	Comfortable
540 tok/s 324–864 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	2.4 GB	Q8_0	Comfortable
540 tok/s 324–864 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	2.4 GB	Q8_0	Comfortable
540 tok/s 324–864 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	2.4 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: Zyphra
Organisation type: Industry
Country: United States of America
Published: 10 February 2025

What it does

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

Domain: Speech
Task: Text-to-speech (TTS), Speech synthesis

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: 1.6B
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: Zyphra

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: Beta Release of Zonos-v0.1
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Zonos-v0.1

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 Zonos-v0.1

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

2.4 GB

Fastest

2,118 tok/s

Zonos-v0.1 is small enough at 1.6B 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 23.0 tokens per second.

The quickest result comes from a B200 at around 2,118 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

What this model is

Zonos-v0.1 was published by Zyphra, in United States of America, in February 2025. The organisation is categorised as industry.

It works in Speech, and is recorded as doing text-to-speech (TTS), Speech synthesis.

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

What decides the speed

Half the cards that hold it manage more than 59.5 tokens per second, and 794 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.

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.

Step by step

How to choose a GPU for Zonos-v0.1

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 Zonos-v0.1 — around 2.4 GB at Q8_0. That figure, not the card's headline performance, is what decides whether it runs.
02

Decide how long your conversations run

The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context Zonos-v0.1 can slip off a card that handles short questions easily.
03

Decide how much compression you will accept

Compression is what makes Zonos-v0.1 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 Zonos-v0.1 is effectively an ordering by memory bandwidth, which is why the B200 tops it at 2,118 tok/s.
05

Read the fit column last

Tight means Zonos-v0.1 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 Zonos-v0.1 alone — a card is usually bought for more than one model.

Answers

Zonos-v0.1 — common questions

Is Zonos-v0.1 open source?

Its weights are published, so Zonos-v0.1 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 Zonos-v0.1 have?

Zonos-v0.1 has 1.6B parameters. 1.6B. 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 Zonos-v0.1?

Zonos-v0.1 was published by Zyphra, based in United States of America, categorised as industry.

When was Zonos-v0.1 released?

Zonos-v0.1 was published in February 2025.

What is Zonos-v0.1 used for?

Zonos-v0.1 works in Speech, and is recorded as handling text-to-speech (TTS), Speech synthesis. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download Zonos-v0.1?

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

Can I run Zonos-v0.1 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 Zonos-v0.1 assume it is fully resident.

Would two GPUs run Zonos-v0.1 faster?

A second card roughly doubles the memory available but not the generation rate. With 818 cards already able to run Zonos-v0.1 alone, the case for pairing is weak.

Why does the quantisation differ between cards for Zonos-v0.1?

Each card is shown running the least-compressed copy it can hold, and Zonos-v0.1 appears at 1 different compression levels across the cards that fit it. Bigger cards get the more accurate version.

How accurate are these Zonos-v0.1 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 1,271–3,388 tok/s on the B200 rather than a single number.

What GPU do I need to run Zonos-v0.1?

The smallest card in our catalogue that holds Zonos-v0.1 is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 2.4 GB, and produces roughly 23.0 tokens per second. 818 cards in total can run it.

How fast is Zonos-v0.1 on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 2,118 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 794 of the cards that can run Zonos-v0.1 clear that.

How much VRAM does Zonos-v0.1 need?

About 2.4 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 Zonos-v0.1 on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 2.4 GB and generating roughly 394 tokens per second — a comfortable fit.

Can I run Zonos-v0.1 on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 2.4 GB and generating roughly 242 tokens per second — a comfortable fit.

Can I run Zonos-v0.1 on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 2.4 GB and generating roughly 299 tokens per second — a comfortable fit.

Can I run Zonos-v0.1 on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 2.4 GB and generating roughly 355 tokens per second — a comfortable fit.

Source

Original publication

Record last updated 28 November 2025

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