data2vec (vision) 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 (vision)?

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

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: Vision
Task: Image classification
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
Training data: 251,108,732 tokens
Epochs: 800

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

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
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 (vision)

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 data2vec (vision)

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

Tesla C1080

Memory needed

1.5 GB

Fastest

4,805 tok/s

data2vec (vision) 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 least hardware that works is a Tesla C1080. Its 4 GB is enough at Q8_0 compression, giving roughly 52.3 tokens per second.

At the other end, a B200 generates roughly 4,805 tokens per second on it, on the strength of 8,000 GB/s of memory bandwidth.

Background

data2vec (vision) was published by Meta AI, in United States of America, in January 2022. industry is the category the publisher falls under.

It works in Vision, and is recorded as doing image classification.

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.

Reading the throughput figures

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.

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

Around 251,108,732 tokens went into training it.

Its inclusion criterion is sOTA improvement.

Step by step

How to choose a GPU for data2vec (vision)

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 data2vec (vision) — around 1.5 GB at Q8_0. That figure, not the card's headline performance, is what decides whether it runs.
02

Match the context to your actual use

Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for data2vec (vision).
03

Choose how far you will compress it

Each card runs the least-compressed copy it can hold — Q8_0 on the smallest card that fits. Setting a floor drops the cards that only manage data2vec (vision) by squeezing it further than you would want.
04

Compare tokens per second, not specifications

Sort by speed to see how cards rank for data2vec (vision). It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 4,805 tok/s.
05

Look at the headroom, not just the fit

A tight fit runs data2vec (vision) but leaves nothing spare for a longer conversation; comfortable has headroom. If you expect to grow the context, buy for comfortable.
06

Open the card you have settled on

Every card name links to its own page, which runs the same calculation across the whole model catalogue. Worth a look before buying for data2vec (vision) alone — a card is usually bought for more than one model.

Answers

data2vec (vision) — common questions

How accurate are these data2vec (vision) 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 2,883–7,688 tok/s on the B200 rather than a single number.

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

The smallest card in our catalogue that holds data2vec (vision) 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.

How fast is data2vec (vision) 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 (vision) clear that.

How much VRAM does data2vec (vision) 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.

Can I run data2vec (vision) 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.

Can I run data2vec (vision) 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.

Can I run data2vec (vision) 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.

Can I run data2vec (vision) 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.

Is data2vec (vision) open source?

Its weights are published, so data2vec (vision) 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 data2vec (vision) have?

data2vec (vision) 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.

Who created data2vec (vision)?

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

When was data2vec (vision) released?

data2vec (vision) 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.

What is data2vec (vision) used for?

data2vec (vision) works in Vision, and is recorded as handling image classification. 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 data2vec (vision)?

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

Can I run data2vec (vision) 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 (vision) is rarely worth using. Every figure here assumes the whole model is on the card.

Would two GPUs run data2vec (vision) faster?

Two cards buy memory rather than speed. That matters for data2vec (vision) only if one card cannot hold it — 818 can, so a second adds little.

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

Each card is shown running the least-compressed copy it can hold, and data2vec (vision) appears at 1 different compression levels across the cards that fit it. Bigger cards get the more accurate version.

Source

Original publication

Record last updated 11 February 2026

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