SigLIP 2 TPS calculator

Open weights Google DeepMind 1.1B 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 · 32.3 tok/s

Fastest card

B200

2,972 tok/s · 180 GB

Which GPUs can run SigLIP 2?

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
2,972 tok/s 1,783–4,755 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	1.9 GB	Q8_0	Comfortable
2,972 tok/s 1,783–4,755 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	1.9 GB	Q8_0	Comfortable
2,373 tok/s 1,424–3,797 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	1.9 GB	Q8_0	Comfortable
2,373 tok/s 1,424–3,797 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	1.9 GB	Q8_0	Comfortable
1,898 tok/s 1,139–3,037 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	1.9 GB	Q8_0	Comfortable
1,817 tok/s 1,090–2,907 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.9 GB	Q8_0	Comfortable
1,817 tok/s 1,090–2,907 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.9 GB	Q8_0	Comfortable
1,739 tok/s 1,043–2,782 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	1.9 GB	Q8_0	Comfortable
1,543 tok/s 926–2,469 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	1.9 GB	Q8_0	Comfortable
1,543 tok/s 926–2,469 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	1.9 GB	Q8_0	Comfortable
1,543 tok/s 926–2,469 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	1.9 GB	Q8_0	Comfortable
1,464 tok/s 878–2,342 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	1.9 GB	Q8_0	Comfortable
1,248 tok/s 749–1,997 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.9 GB	Q8_0	Comfortable
1,248 tok/s 749–1,997 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	1.9 GB	Q8_0	Comfortable
1,248 tok/s 749–1,997 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	1.9 GB	Q8_0	Comfortable
1,248 tok/s 749–1,997 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.9 GB	Q8_0	Comfortable
1,248 tok/s 749–1,997 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	1.9 GB	Q8_0	Comfortable
950 tok/s 570–1,521 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	1.9 GB	Q8_0	Comfortable
950 tok/s 570–1,521 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	1.9 GB	Q8_0	Comfortable
792 tok/s 475–1,267 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	1.9 GB	Q8_0	Comfortable
775 tok/s 465–1,240 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	1.9 GB	Q8_0	Comfortable
758 tok/s 455–1,213 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	1.9 GB	Q8_0	Comfortable
758 tok/s 455–1,213 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	1.9 GB	Q8_0	Comfortable
758 tok/s 455–1,213 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	1.9 GB	Q8_0	Comfortable
758 tok/s 455–1,213 · 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: Google DeepMind
Organisation type: Industry
Country: United States of America
Published: 20 February 2025
Authors: Michael Tschannen, Alexey Gritsenko, Xiao Wang, Muhammad Ferjad Naeem, Ibrahim Alabdulmohsin, Nikhil Parthasarathy, Talfan Evans, Lucas Beyer, Ye Xia, Basil Mustafa, Olivier Hénaff, Jeremiah Harmsen, Andreas Steiner, Xiaohua Zhai

What it does

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

Domain: Vision
Task: Image classification, Image embedding

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

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: 8.2 × 10²² FLOP
How it was established: Operation counting

The training run

What it physically took to train: which chips, how many, for how long, and what that drew from the wall.

Training hardware: Google TPU v5e
Chips used: 2,048
Power draw: 904.7 kW

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: google

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: SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run SigLIP 2

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 SigLIP 2

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

1.9 GB

Fastest

2,972 tok/s

SigLIP 2 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 least hardware that works is a Tesla C1080. Its 4 GB is enough at Q8_0 compression, giving roughly 32.3 tokens per second.

Top of the range is the B200, at roughly 2,972 tokens per second thanks to 8,000 GB/s of bandwidth.

About this model

SigLIP 2 was published by Google DeepMind, in United States of America, in February 2025. The organisation is categorised as industry.

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

Published weights mean the model runs on your machine rather than someone else's, which is what makes the hardware question below answerable at all. It is published under the google organisation on Hugging Face.

How fast it runs, and why

The median result is around 83.5 tokens per second; 799 cards produce text faster than most people read it.

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.

Training and provenance

Producing it required around 8.2 × 10²² FLOP of arithmetic, on Google TPU v5e, which is a statement about the training budget rather than about inference.

Step by step

How to choose a GPU for SigLIP 2

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 SigLIP 2 actually needs — around 1.9 GB at Q8_0. No amount of processing power compensates for a card that cannot hold it.
02

Decide how long your conversations run

Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for SigLIP 2.
03

Choose how far you will compress it

Compression is what makes SigLIP 2 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

Sort by speed to see how cards rank for SigLIP 2. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 2,972 tok/s.
05

Check the fit verdict before buying

The fit column separates cards that just manage SigLIP 2 from those with room to spare. Buy for the second if the context might grow.
06

Open the card you have settled on

Following a card through to its own page shows every other model it can hold, which is the question that follows once SigLIP 2 is settled.

Answers

SigLIP 2 — common questions

When was SigLIP 2 released?

SigLIP 2 was published in February 2025.

What is SigLIP 2 used for?

SigLIP 2 works in Vision, and is recorded as handling image classification, Image embedding. 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 SigLIP 2?

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

How much compute was used to train SigLIP 2?

Around 8.2 × 10²² FLOP, on Google TPU v5e. 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.

Can I run SigLIP 2 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 SigLIP 2 is rarely worth using. Every figure here assumes the whole model is on the card.

Would two GPUs run SigLIP 2 faster?

A second card roughly doubles the memory available but not the generation rate. With 818 cards already able to run SigLIP 2 alone, the case for pairing is weak.

Why does the quantisation differ between cards for SigLIP 2?

A larger card holds a more accurate copy. Across the cards that run SigLIP 2, 1 compression levels are used; the floor control above pins it to one.

How accurate are these SigLIP 2 speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 1,783–4,755 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.

What GPU do I need to run SigLIP 2?

The smallest card in our catalogue that holds SigLIP 2 is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 1.9 GB, and produces roughly 32.3 tokens per second. 818 cards in total can run it.

How fast is SigLIP 2 on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 2,972 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 SigLIP 2 clear that.

How much VRAM does SigLIP 2 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 SigLIP 2 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 554 tokens per second — a comfortable fit.

Can I run SigLIP 2 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 339 tokens per second — a comfortable fit.

Can I run SigLIP 2 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 420 tokens per second — a comfortable fit.

Can I run SigLIP 2 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 498 tokens per second — a comfortable fit.

Is SigLIP 2 open source?

Its weights are published, so SigLIP 2 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 SigLIP 2 have?

SigLIP 2 has 1.1B parameters. 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.

Who created SigLIP 2?

SigLIP 2 was published by Google DeepMind, based in United States of America, categorised as industry.

Source

Original publication

Record last updated 28 November 2025

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