Parakeet ASR rnnt 1.1B TPS calculator

Open weights NVIDIA 1.1B parameters April 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 · 33.5 tok/s

Fastest card

B200

3,080 tok/s · 180 GB

Which GPUs can run Parakeet ASR rnnt 1.1B?

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
3,080 tok/s 1,848–4,928 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	1.9 GB	Q8_0	Comfortable
3,080 tok/s 1,848–4,928 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	1.9 GB	Q8_0	Comfortable
2,460 tok/s 1,476–3,935 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	1.9 GB	Q8_0	Comfortable
2,460 tok/s 1,476–3,935 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	1.9 GB	Q8_0	Comfortable
1,967 tok/s 1,180–3,147 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	1.9 GB	Q8_0	Comfortable
1,883 tok/s 1,130–3,012 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.9 GB	Q8_0	Comfortable
1,883 tok/s 1,130–3,012 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.9 GB	Q8_0	Comfortable
1,802 tok/s 1,081–2,883 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	1.9 GB	Q8_0	Comfortable
1,599 tok/s 960–2,559 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	1.9 GB	Q8_0	Comfortable
1,599 tok/s 960–2,559 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	1.9 GB	Q8_0	Comfortable
1,599 tok/s 960–2,559 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	1.9 GB	Q8_0	Comfortable
1,517 tok/s 910–2,427 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	1.9 GB	Q8_0	Comfortable
1,294 tok/s 776–2,070 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.9 GB	Q8_0	Comfortable
1,294 tok/s 776–2,070 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	1.9 GB	Q8_0	Comfortable
1,294 tok/s 776–2,070 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	1.9 GB	Q8_0	Comfortable
1,294 tok/s 776–2,070 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.9 GB	Q8_0	Comfortable
1,294 tok/s 776–2,070 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	1.9 GB	Q8_0	Comfortable
985 tok/s 591–1,576 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	1.9 GB	Q8_0	Comfortable
985 tok/s 591–1,576 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	1.9 GB	Q8_0	Comfortable
821 tok/s 493–1,313 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	1.9 GB	Q8_0	Comfortable
803 tok/s 482–1,285 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	1.9 GB	Q8_0	Comfortable
785 tok/s 471–1,257 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	1.9 GB	Q8_0	Comfortable
785 tok/s 471–1,257 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	1.9 GB	Q8_0	Comfortable
785 tok/s 471–1,257 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	1.9 GB	Q8_0	Comfortable
785 tok/s 471–1,257 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	1.9 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: NVIDIA
Organisation type: Industry
Country: United States of America
Published: 18 April 2024
Authors: Somshubra Majumdar, Nithin Rao Koluguri

What it does

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

Domain: Speech
Task: Speech recognition (ASR), Speech-to-text

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: 1.1B
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 (unrestricted)
Training code: Unreleased
Hugging Face: nvidia

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: Pushing the Boundaries of Speech Recognition with NVIDIA NeMo Parakeet ASR Models
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Parakeet ASR rnnt 1.1B

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 Parakeet ASR rnnt 1.1B

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

Hardware requirements in practice

Minimum card

Tesla C1080

Memory needed

1.9 GB

Fastest

3,080 tok/s

Parakeet ASR rnnt 1.1B is small enough at 1.1B parameters that hardware is rarely the obstacle — 818 of the cards we track can run it, including cards several years old.

The entry point is the Tesla C1080: 4 GB of memory, Q8_0 compression, roughly 33.5 tokens per second.

The quickest result comes from a B200 at around 3,080 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

What this model is

Parakeet ASR rnnt 1.1B was published by NVIDIA, in United States of America, in April 2024. The organisation is categorised as industry.

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

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 nvidia organisation on Hugging Face.

What decides the speed

Across every card that can run it, the middle of the range is about 86.5 tokens per second, and 799 of them clear the ten tokens per second that roughly matches reading speed.

Every weight participates in every token here, so bandwidth is the whole story: the ranking below is effectively a ranking of memory throughput.

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 Parakeet ASR rnnt 1.1B

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 Parakeet ASR rnnt 1.1B actually needs — around 1.9 GB at Q8_0. No amount of processing power compensates for a card that cannot hold it.
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 Parakeet ASR rnnt 1.1B.
03

Choose how far you will compress it

Compression is what makes Parakeet ASR rnnt 1.1B 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

Sort by speed

Sort by speed to see how cards rank for Parakeet ASR rnnt 1.1B. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 3,080 tok/s.
05

Read the fit column last

The fit column separates cards that just manage Parakeet ASR rnnt 1.1B from those with room to spare. Buy for the second if the context might grow.
06

Open the card you have settled on

Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond Parakeet ASR rnnt 1.1B.

Answers

Parakeet ASR rnnt 1.1B — common questions

Who created Parakeet ASR rnnt 1.1B?

Parakeet ASR rnnt 1.1B was published by NVIDIA, based in United States of America, categorised as industry.

When was Parakeet ASR rnnt 1.1B released?

Parakeet ASR rnnt 1.1B was published in April 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 Parakeet ASR rnnt 1.1B used for?

Parakeet ASR rnnt 1.1B works in Speech, and is recorded as handling speech recognition (ASR), Speech-to-text. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download Parakeet ASR rnnt 1.1B?

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

Can I run Parakeet ASR rnnt 1.1B 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 Parakeet ASR rnnt 1.1B assume it is fully resident.

Would two GPUs run Parakeet ASR rnnt 1.1B faster?

Capacity adds across cards; throughput does not. Since 818 of the cards we track already hold Parakeet ASR rnnt 1.1B on their own, a second card is rarely the answer here.

Why does the quantisation differ between cards for Parakeet ASR rnnt 1.1B?

Because capacity varies, so does how hard Parakeet ASR rnnt 1.1B has to be squeezed — 1 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these Parakeet ASR rnnt 1.1B 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 1,848–4,928 tok/s on the B200 rather than a single number.

What GPU do I need to run Parakeet ASR rnnt 1.1B?

The smallest card in our catalogue that holds Parakeet ASR rnnt 1.1B is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 1.9 GB, and produces roughly 33.5 tokens per second. 818 cards in total can run it.

How fast is Parakeet ASR rnnt 1.1B on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 3,080 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 799 of the cards that can run Parakeet ASR rnnt 1.1B clear that.

How much VRAM does Parakeet ASR rnnt 1.1B need?

About 1.9 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 Parakeet ASR rnnt 1.1B on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 1.9 GB and generating roughly 574 tokens per second — a comfortable fit.

Can I run Parakeet ASR rnnt 1.1B on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 1.9 GB and generating roughly 351 tokens per second — a comfortable fit.

Can I run Parakeet ASR rnnt 1.1B on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 1.9 GB and generating roughly 435 tokens per second — a comfortable fit.

Can I run Parakeet ASR rnnt 1.1B on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 1.9 GB and generating roughly 516 tokens per second — a comfortable fit.

Is Parakeet ASR rnnt 1.1B open source?

Its weights are published, so Parakeet ASR rnnt 1.1B 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 Parakeet ASR rnnt 1.1B have?

Parakeet ASR rnnt 1.1B has 1.1B parameters. 1.1B. 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.

Source

Original publication

Record last updated 28 November 2025

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