data2vec (speech) TPS calculator

Open weights Meta AI 705.1M parameters January 2022

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 · 52.3 tok/s

Fastest card

B200

4,805 tok/s · 180 GB

Which GPUs can run data2vec (speech)?

Set the inputs, read the answer

A longer conversation needs more memory, which can push this model off smaller cards.

Hides cards that would only fit the model by compressing it below this point.

818 cards match

Calculating
Needs Quantisation Fit
4,805 tok/s

2,883–7,688 · low confidence

B200 NVIDIA 180 GB 8,000 GB/s Jan 2024 1.5 GB Q8_0 Comfortable
4,805 tok/s

2,883–7,688 · low confidence

B300 NVIDIA 288 GB 8,000 GB/s Sep 2025 1.5 GB Q8_0 Comfortable
3,837 tok/s

2,302–6,139 · low confidence

Radeon Instinct MI350X AMD 288 GB 8,190 GB/s Jan 2025 1.5 GB Q8_0 Comfortable
3,837 tok/s

2,302–6,139 · low confidence

Radeon Instinct MI355X AMD 288 GB 8,190 GB/s Jan 2025 1.5 GB Q8_0 Comfortable
3,069 tok/s

1,841–4,910 · low confidence

Radeon Instinct MI300 AMD 128 GB 6,550 GB/s Jan 2023 1.5 GB Q8_0 Comfortable
2,937 tok/s

1,762–4,699 · low confidence

H200 NVL NVIDIA 141 GB 4,890 GB/s Nov 2024 1.5 GB Q8_0 Comfortable
2,937 tok/s

1,762–4,699 · low confidence

H200 SXM 141 GB NVIDIA 141 GB 4,890 GB/s Nov 2024 1.5 GB Q8_0 Comfortable
2,811 tok/s

1,687–4,498 · low confidence

Radeon Instinct MI325X AMD 256 GB 6,000 GB/s Oct 2024 1.5 GB Q8_0 Comfortable
2,495 tok/s

1,497–3,992 · low confidence

Radeon Instinct MI300A AMD 128 GB 5,325 GB/s Dec 2023 1.5 GB Q8_0 Comfortable
2,495 tok/s

1,497–3,992 · low confidence

Radeon Instinct MI300X AMD 192 GB 5,325 GB/s Dec 2023 1.5 GB Q8_0 Comfortable
2,495 tok/s

1,497–3,992 · low confidence

Radeon Instinct MI308X AMD 192 GB 5,325 GB/s Dec 2023 1.5 GB Q8_0 Comfortable
2,367 tok/s

1,420–3,786 · low confidence

H100 NVL 94 GB NVIDIA 94 GB 3,940 GB/s Mar 2023 1.5 GB Q8_0 Comfortable
2,018 tok/s

1,211–3,229 · low confidence

H100 PCIe 96 GB NVIDIA 96 GB 3,360 GB/s Mar 2023 1.5 GB Q8_0 Comfortable
2,018 tok/s

1,211–3,229 · low confidence

H100 SXM5 80 GB NVIDIA 80 GB 3,360 GB/s Oct 2022 1.5 GB Q8_0 Comfortable
2,018 tok/s

1,211–3,229 · low confidence

H100 SXM5 94 GB NVIDIA 94 GB 3,360 GB/s Mar 2023 1.5 GB Q8_0 Comfortable
2,018 tok/s

1,211–3,229 · low confidence

H100 SXM5 96 GB NVIDIA 96 GB 3,360 GB/s Mar 2023 1.5 GB Q8_0 Comfortable
2,018 tok/s

1,211–3,229 · low confidence

H800 SXM5 NVIDIA 80 GB 3,360 GB/s Mar 2023 1.5 GB Q8_0 Comfortable
1,537 tok/s

922–2,459 · low confidence

Radeon Instinct MI250 AMD 128 GB 3,280 GB/s Nov 2021 1.5 GB Q8_0 Comfortable
1,537 tok/s

922–2,459 · low confidence

Radeon Instinct MI250X AMD 128 GB 3,280 GB/s Nov 2021 1.5 GB Q8_0 Comfortable
1,281 tok/s

768–2,049 · low confidence

Data Center GPU Max 1550 Intel 128 GB 3,280 GB/s Jan 2023 1.5 GB Q8_0 Comfortable
1,253 tok/s

752–2,005 · low confidence

Data Center GPU Max Subsystem Intel 128 GB 3,210 GB/s Jan 2023 1.5 GB Q8_0 Comfortable
1,225 tok/s

735–1,960 · low confidence

A100 SXM4 80 GB NVIDIA 80 GB 2,040 GB/s Nov 2020 1.5 GB Q8_0 Comfortable
1,225 tok/s

735–1,960 · low confidence

A100X NVIDIA 80 GB 2,040 GB/s Jun 2021 1.5 GB Q8_0 Comfortable
1,225 tok/s

735–1,960 · low confidence

A800 SXM4 80 GB NVIDIA 80 GB 2,040 GB/s Aug 2022 1.5 GB Q8_0 Comfortable
1,225 tok/s

735–1,960 · low confidence

H100 CNX NVIDIA 80 GB 2,040 GB/s Mar 2023 1.5 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
Meta AI
Organisation type
Industry
Country
United States of America
Published
20 January 2022
Authors
Alexei Baevski, Wei-Ning Hsu, Qiantong Xu , Arun Babu, Jiatao Gu, Michael Auli

What it does

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

Domain
Speech
Task
Speech recognition (ASR)
Approach
Self-supervised learning
Numerical format
FP16

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
705.1M

Section 4: "We experiment with two model sizes: data2vec Base and data2vec Large, containing either L = 12 or L = 24 Trans- former blocks with H = 768 or H = 1024 hidden dimen- sion (with 4 × H feed-forward inner-dimension)"

Training data
17,510,400 tokens

Section 5.2: "we pre-train data2vec on the 960 hours of speech audio data from Librispeech (LS-960)" 13,680 words per hour

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
Open source

MIT license https://github.com/facebookresearch/fairseq/tree/main/examples/data2vec

How it is classified

Labels the source dataset applies when tracking notable models, and how confident it is in the entry.

Why it is tracked
SOTA improvement

"Experiments on the major benchmarks of speech recognition, image classification, and natural lan guage understanding demonstrate a new state of the art or competitive performance to predominant approaches"

Citations
1,021

Sources

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

Reference
Data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
Last updated
11 February 2026

The extremes

The ten fastest GPUs that run data2vec (speech)

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.

  1. 01 B300 288 GB · 8,000 GB/s · Q8_0 4,805 tok/s
  2. 02 B200 180 GB · 8,000 GB/s · Q8_0 4,805 tok/s
  3. 03 Radeon Instinct MI350X 288 GB · 8,190 GB/s · Q8_0 3,837 tok/s
  4. 04 Radeon Instinct MI355X 288 GB · 8,190 GB/s · Q8_0 3,837 tok/s
  5. 05 Radeon Instinct MI300 128 GB · 6,550 GB/s · Q8_0 3,069 tok/s
  6. 06 H200 NVL 141 GB · 4,890 GB/s · Q8_0 2,937 tok/s
  7. 07 H200 SXM 141 GB 141 GB · 4,890 GB/s · Q8_0 2,937 tok/s
  8. 08 Radeon Instinct MI325X 256 GB · 6,000 GB/s · Q8_0 2,811 tok/s
  9. 09 Radeon Instinct MI300A 128 GB · 5,325 GB/s · Q8_0 2,495 tok/s
  10. 10 Radeon Instinct MI300X 192 GB · 5,325 GB/s · Q8_0 2,495 tok/s

The smallest GPUs that still run data2vec (speech)

The cheapest route in, by memory capacity. A tight fit runs the model but leaves nothing spare for a longer conversation.

  1. 01 GeForce RTX 4010 4 GB · needs 1.5 GB · Q8_0 · comfortable 57.7 tok/s
  2. 02 RTX A400 4 GB · needs 1.5 GB · Q8_0 · comfortable 57.7 tok/s
  3. 03 RTX 500 Mobile Ada Generation 4 GB · needs 1.5 GB · Q8_0 · comfortable 76.9 tok/s
  4. 04 GeForce RTX 3050 A Mobile 4 GB · needs 1.5 GB · Q8_0 · comfortable 115 tok/s
  5. 05 Jetson Orin Nano 4 GB 4 GB · needs 1.5 GB · Q8_0 · comfortable 20.5 tok/s
  6. 06 Radeon RX 6450M 4 GB · needs 1.5 GB · Q8_0 · comfortable 60.0 tok/s
  7. 07 Radeon RX 6550M 4 GB · needs 1.5 GB · Q8_0 · comfortable 67.5 tok/s
  8. 08 Radeon RX 6550S 4 GB · needs 1.5 GB · Q8_0 · comfortable 60.0 tok/s
  9. 09 Arc A310 4 GB · needs 1.5 GB · Q8_0 · comfortable 48.4 tok/s
  10. 10 Arc Pro A30M 4 GB · needs 1.5 GB · Q8_0 · comfortable 50.0 tok/s

What the numbers mean

Hardware requirements in practice

Minimum card

Tesla C1080

Memory needed

1.5 GB

Fastest

4,805 tok/s

data2vec (speech) is small enough at 705.1M 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 52.3 tokens per second.

A B200 is the fastest we calculate for it: about 4,805 tokens per second, from 8,000 GB/s of memory bandwidth.

About this model

data2vec (speech) was published by Meta AI, in United States of America, in January 2022. It comes out of industry.

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

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.

How fast it runs, and why

Across every card that can run it, the middle of the range is about 134.9 tokens per second, and 809 of them clear the ten tokens per second that roughly matches reading speed.

Being dense, it reads all of itself per token, which is why the ordering by speed below follows the ordering by memory bandwidth so closely.

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.

What went into building it

Around 17,510,400 tokens went into training it.

The reason it appears in this catalogue at all is sOTA improvement.

Step by step

How to choose a GPU for data2vec (speech)

The table above has already assessed every card we hold specifications for against this model. Getting to your answer takes six steps.

  1. 01

    Start from the memory column

    The table lists every card that can hold data2vec (speech) — around 1.5 GB at Q8_0. That figure, not the card's headline performance, is what decides whether it runs.

  2. 02

    Match the context to your actual use

    The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context data2vec (speech) can slip off a card that handles short questions easily.

  3. 03

    Decide how much compression you will accept

    The quantisation column varies by card, because a bigger card holds a more accurate copy of data2vec (speech) — Q8_0 on the smallest card that fits. Set a floor to hold the comparison at one level.

  4. 04

    Sort by speed

    The speed ordering for data2vec (speech) is effectively an ordering by memory bandwidth, which is why the B200 tops it at 4,805 tok/s.

  5. 05

    Check the fit verdict before buying

    The fit column separates cards that just manage data2vec (speech) from those with room to spare. Buy for the second if the context might grow.

  6. 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 data2vec (speech).

Answers

data2vec (speech) — common questions

01

Can I run data2vec (speech) if it does not fit in my GPU?

Partly. Layers that do not fit sit in system memory and run at a fraction of the speed, so a mostly-offloaded data2vec (speech) is rarely worth using. Every figure here assumes the whole model is on the card.

02

Would two GPUs run data2vec (speech) faster?

Capacity adds across cards; throughput does not. Since 818 of the cards we track already hold data2vec (speech) on their own, a second card is rarely the answer here.

03

Why does the quantisation differ between cards for data2vec (speech)?

A larger card holds a more accurate copy. Across the cards that run data2vec (speech), 1 compression levels are used; the floor control above pins it to one.

04

How accurate are these data2vec (speech) speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 2,883–7,688 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.

05

What GPU do I need to run data2vec (speech)?

The smallest card in our catalogue that holds data2vec (speech) is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 1.5 GB, and produces roughly 52.3 tokens per second. 818 cards in total can run it.

06

How fast is data2vec (speech) on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 4,805 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 809 of the cards that can run data2vec (speech) clear that.

07

How much VRAM does data2vec (speech) need?

About 1.5 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.

08

Can I run data2vec (speech) on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 1.5 GB and generating roughly 895 tokens per second — a comfortable fit.

09

Can I run data2vec (speech) on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 1.5 GB and generating roughly 548 tokens per second — a comfortable fit.

10

Can I run data2vec (speech) on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 1.5 GB and generating roughly 679 tokens per second — a comfortable fit.

11

Can I run data2vec (speech) on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 1.5 GB and generating roughly 805 tokens per second — a comfortable fit.

12

Is data2vec (speech) open source?

Its weights are published, so data2vec (speech) 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.

13

How many parameters does data2vec (speech) have?

data2vec (speech) has 705.1M parameters. Section 4: "We experiment with two model sizes: data2vec Base and data2vec Large, containing either L = 12 or L = 24 Trans- former blocks with H = 768 or H = 1024 hidden dimen- sion (with 4 × H feed-forward inner-dimension)". 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.

14

Who created data2vec (speech)?

data2vec (speech) was published by Meta AI, based in United States of America, categorised as industry.

15

When was data2vec (speech) released?

data2vec (speech) was published in January 2022. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.

16

What is data2vec (speech) used for?

data2vec (speech) works in Speech, and is recorded as handling speech recognition (ASR). Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

17

Where can I download data2vec (speech)?

The weights for data2vec (speech) are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.

Source

Original publication

Record last updated 11 February 2026

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