PaliGemma 2 3B Mix 448 TPS calculator

Open weights Google 2.9B 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 · Q6_K · 18.3 tok/s

Fastest card

B200

1,160 tok/s · 180 GB

Which GPUs can run PaliGemma 2 3B Mix 448?

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
1,160 tok/s 696–1,857 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	3.8 GB	Q8_0	Comfortable
1,160 tok/s 696–1,857 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	3.8 GB	Q8_0	Comfortable
927 tok/s 556–1,483 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	3.8 GB	Q8_0	Comfortable
927 tok/s 556–1,483 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	3.8 GB	Q8_0	Comfortable
741 tok/s 445–1,186 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	3.8 GB	Q8_0	Comfortable
709 tok/s 426–1,135 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	3.8 GB	Q8_0	Comfortable
709 tok/s 426–1,135 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	3.8 GB	Q8_0	Comfortable
679 tok/s 407–1,086 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	3.8 GB	Q8_0	Comfortable
602 tok/s 361–964 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	3.8 GB	Q8_0	Comfortable
602 tok/s 361–964 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	3.8 GB	Q8_0	Comfortable
602 tok/s 361–964 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	3.8 GB	Q8_0	Comfortable
571 tok/s 343–914 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	3.8 GB	Q8_0	Comfortable
487 tok/s 292–780 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	3.8 GB	Q8_0	Comfortable
487 tok/s 292–780 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	3.8 GB	Q8_0	Comfortable
487 tok/s 292–780 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	3.8 GB	Q8_0	Comfortable
487 tok/s 292–780 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	3.8 GB	Q8_0	Comfortable
487 tok/s 292–780 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	3.8 GB	Q8_0	Comfortable
371 tok/s 223–594 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	3.8 GB	Q8_0	Comfortable
371 tok/s 223–594 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	3.8 GB	Q8_0	Comfortable
309 tok/s 186–495 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	3.8 GB	Q8_0	Comfortable
303 tok/s 182–484 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	3.8 GB	Q8_0	Comfortable
296 tok/s 178–473 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	3.8 GB	Q8_0	Comfortable
296 tok/s 178–473 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	3.8 GB	Q8_0	Comfortable
296 tok/s 178–473 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	3.8 GB	Q8_0	Comfortable
296 tok/s 178–473 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	3.8 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
Organisation type: Industry
Country: United States of America
Published: 19 February 2025
Authors: Omar Sanseviero, Andreas Steiner

What it does

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

Domain: Vision, Multimodal, Language
Task: Image captioning, Visual question answering, Object detection, Object recognition, Image segmentation, Character recognition (OCR)
Base model: PaliGemma 2 3B Mix 224

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: 2.9B
Training data: 51,200,000,000 tokens

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: 256
Power draw: 113.1 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 (non-commercial)
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: Introducing PaliGemma 2 mix: A vision-language model for multiple tasks
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run PaliGemma 2 3B Mix 448

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 PaliGemma 2 3B Mix 448

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

3.1 GB

Fastest

1,160 tok/s

PaliGemma 2 3B Mix 448 is small enough at 2.9B 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 Q6_K compression, giving roughly 18.3 tokens per second.

At the other end, a B200 generates roughly 1,160 tokens per second on it, on the strength of 8,000 GB/s of memory bandwidth.

Where it came from

PaliGemma 2 3B Mix 448 was published by Google, in United States of America, in February 2025. The organisation is categorised as industry.

It works in Vision, Multimodal, Language, and is recorded as doing image captioning, Visual question answering, Object detection, Object recognition, Image segmentation, Character recognition (OCR).

It builds on PaliGemma 2 3B Mix 224, which is why it shares that model's general shape and size.

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.

Understanding the speeds

Half the cards that hold it manage more than 36.8 tokens per second, and 783 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.

What went into building it

It was trained on about 51,200,000,000 tokens of text.

Step by step

How to choose a GPU for PaliGemma 2 3B Mix 448

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 PaliGemma 2 3B Mix 448 actually needs — around 3.1 GB at Q6_K. No amount of processing power compensates for a card that cannot hold it.
02

Decide how long your conversations run

Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason PaliGemma 2 3B Mix 448 stops fitting a card that seemed fine.
03

Set a quality floor

Each card runs the least-compressed copy it can hold — Q6_K on the smallest card that fits. Setting a floor drops the cards that only manage PaliGemma 2 3B Mix 448 by squeezing it further than you would want.
04

Sort by speed

The speed ordering for PaliGemma 2 3B Mix 448 is effectively an ordering by memory bandwidth, which is why the B200 tops it at 1,160 tok/s.
05

Check the fit verdict before buying

The fit column separates cards that just manage PaliGemma 2 3B Mix 448 from those with room to spare. Buy for the second if the context might grow.
06

See what else that card runs

Following a card through to its own page shows every other model it can hold, which is the question that follows once PaliGemma 2 3B Mix 448 is settled.

Answers

PaliGemma 2 3B Mix 448 — common questions

Can I run PaliGemma 2 3B Mix 448 on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 3.8 GB and generating roughly 194 tokens per second — a comfortable fit.

Is PaliGemma 2 3B Mix 448 open source?

Its weights are published, so PaliGemma 2 3B Mix 448 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 PaliGemma 2 3B Mix 448 have?

PaliGemma 2 3B Mix 448 has 2.9B parameters. https://huggingface.co/google/paligemma-3b-mix-448. 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 PaliGemma 2 3B Mix 448?

PaliGemma 2 3B Mix 448 was published by Google, based in United States of America, categorised as industry.

When was PaliGemma 2 3B Mix 448 released?

PaliGemma 2 3B Mix 448 was published in February 2025.

What is PaliGemma 2 3B Mix 448 used for?

PaliGemma 2 3B Mix 448 works in Vision, Multimodal, Language, and is recorded as handling image captioning, Visual question answering, Object detection, Object recognition, Image segmentation, Character recognition (OCR). Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Where can I download PaliGemma 2 3B Mix 448?

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.

Can I run PaliGemma 2 3B Mix 448 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 PaliGemma 2 3B Mix 448 assume it is fully resident.

Would two GPUs run PaliGemma 2 3B Mix 448 faster?

Capacity adds across cards; throughput does not. Since 818 of the cards we track already hold PaliGemma 2 3B Mix 448 on their own, a second card is rarely the answer here.

Why does the quantisation differ between cards for PaliGemma 2 3B Mix 448?

A larger card holds a more accurate copy. Across the cards that run PaliGemma 2 3B Mix 448, 2 compression levels are used; the floor control above pins it to one.

How accurate are these PaliGemma 2 3B Mix 448 speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 696–1,857 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 PaliGemma 2 3B Mix 448?

The smallest card in our catalogue that holds PaliGemma 2 3B Mix 448 is the Tesla C1080, with 4 GB of memory. It runs the model at Q6_K using about 3.1 GB, and produces roughly 18.3 tokens per second. 818 cards in total can run it.

How fast is PaliGemma 2 3B Mix 448 on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 1,160 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 783 of the cards that can run PaliGemma 2 3B Mix 448 clear that.

How much VRAM does PaliGemma 2 3B Mix 448 need?

About 3.1 GB at Q6_K 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 PaliGemma 2 3B Mix 448 on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 3.8 GB and generating roughly 216 tokens per second — a comfortable fit.

Can I run PaliGemma 2 3B Mix 448 on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 3.8 GB and generating roughly 132 tokens per second — a comfortable fit.

Can I run PaliGemma 2 3B Mix 448 on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 3.8 GB and generating roughly 164 tokens per second — a comfortable fit.

Source

Original publication

Record last updated 28 November 2025

The other direction

Looking at it from the other side?

This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.

All AI models