Gemma 3 4B TPS calculator

Open weights Google DeepMind 4B parameters March 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 of 818 cards that can run it

Smallest card that fits

Tesla C1080

4 GB · Q4_K_M · 21.3 tok/s

Fastest card

B200

847 tok/s · 180 GB

Which GPUs can run Gemma 3 4B?

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
847 tok/s 720–1,016	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	5.0 GB	Q8_0	Comfortable
847 tok/s 720–1,016	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	5.0 GB	Q8_0	Comfortable
676 tok/s 406–1,082 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	5.0 GB	Q8_0	Comfortable
676 tok/s 406–1,082 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	5.0 GB	Q8_0	Comfortable
541 tok/s 325–866 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	5.0 GB	Q8_0	Comfortable
518 tok/s 440–621	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	5.0 GB	Q8_0	Comfortable
518 tok/s 440–621	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	5.0 GB	Q8_0	Comfortable
496 tok/s 297–793 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	5.0 GB	Q8_0	Comfortable
440 tok/s 264–704 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	5.0 GB	Q8_0	Comfortable
440 tok/s 264–704 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	5.0 GB	Q8_0	Comfortable
440 tok/s 264–704 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	5.0 GB	Q8_0	Comfortable
417 tok/s 355–501	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	5.0 GB	Q8_0	Comfortable
356 tok/s 302–427	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	5.0 GB	Q8_0	Comfortable
356 tok/s 302–427	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	5.0 GB	Q8_0	Comfortable
356 tok/s 302–427	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	5.0 GB	Q8_0	Comfortable
356 tok/s 302–427	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	5.0 GB	Q8_0	Comfortable
356 tok/s 302–427	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	5.0 GB	Q8_0	Comfortable
271 tok/s 163–433 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	5.0 GB	Q8_0	Comfortable
271 tok/s 163–433 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	5.0 GB	Q8_0	Comfortable
226 tok/s 135–361 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	5.0 GB	Q8_0	Comfortable
221 tok/s 133–353 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	5.0 GB	Q8_0	Comfortable
216 tok/s 184–259	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	5.0 GB	Q8_0	Comfortable
216 tok/s 184–259	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	5.0 GB	Q8_0	Comfortable
216 tok/s 184–259	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	5.0 GB	Q8_0	Comfortable
216 tok/s 184–259	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	5.0 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: 12 March 2025
Authors: Core contributors: Aishwarya Kamath, Johan Ferret, Shreya Pathak, Nino Vieillard, Ramona Merhej, Sarah Perrin, Tatiana Matejovicova, Alexandre Ramé, Morgane Rivière, Louis Rouillard, Thomas Mesnard, Geoffrey Cideron, Jean-bastien Grill, Sabela Ramos, Edouard Yvinec, Michelle Casbon, Etienne Pot, Ivo Penchev, Gaël Liu, Francesco Visin, Kathleen Kenealy, Lucas Beyer, Xiaohai Zhai, Anton Tsitsulin, R…

What it does

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

Domain: Language, Vision, Multimodal
Task: Language modeling/generation, Question answering, Translation, Chat, Quantitative reasoning, Visual question answering, Code generation
Base model: SigLIP 400M

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: 4B
Training data: 4,000,000,000,000 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: 9.6 × 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: 904.3 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 (restricted use)
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.

Likely above 10²³ FLOP: Yes
Record confidence: Confident

Sources

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

Reference: Gemma 3 Technical Report
Last updated: 28 November 2025

The extremes

The ten fastest GPUs for Gemma 3 4B

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 Gemma 3 4B

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 it takes to run this model

Minimum card

Tesla C1080

Memory needed

3.2 GB

Fastest

847 tok/s

Gemma 3 4B is small enough at 4B parameters that hardware is rarely the obstacle — 818 of the cards we track can run it, including cards several years old.

The smallest card that holds it is the Tesla C1080 with 4 GB, running it at Q4_K_M and producing around 21.3 tokens per second.

Top of the range is the B200, at roughly 847 tokens per second thanks to 8,000 GB/s of bandwidth.

Where it came from

Gemma 3 4B was published by Google DeepMind, in United States of America, in March 2025. It comes out of industry.

It works in Language, Vision, Multimodal, and is recorded as doing language modeling/generation, Question answering, Translation, Chat, Quantitative reasoning, Visual question answering, Code generation.

Its starting point was SigLIP 400M — most models at this scale are adapted from an existing base rather than built from nothing.

The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached. It is published under the google organisation on Hugging Face.

Understanding the speeds

The median result is around 31.3 tokens per second; 779 cards produce text faster than most people read it.

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 attention layout is on file, so the memory figures are computed exactly rather than approximated.

What went into building it

The training run consumed about 9.6 × 10²² FLOP, on Google TPU v5e. That figure describes the cost of creating it and has no bearing on how quickly it generates text.

The training set ran to roughly 4,000,000,000,000 tokens.

Step by step

How to choose a GPU for Gemma 3 4B

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

The table lists every card that can hold Gemma 3 4B — around 3.2 GB at Q4_K_M. That figure, not the card's headline performance, is what decides whether it runs.
02

Set the context length you will work at

The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context Gemma 3 4B can slip off a card that handles short questions easily.
03

Choose how far you will compress it

The quantisation column varies by card, because a bigger card holds a more accurate copy of Gemma 3 4B — Q4_K_M on the smallest card that fits. Set a floor to hold the comparison at one level.
04

Compare tokens per second, not specifications

The speed ordering for Gemma 3 4B is effectively an ordering by memory bandwidth, which is why the B200 tops it at 847 tok/s.
05

Look at the headroom, not just the fit

The fit column separates cards that just manage Gemma 3 4B from those with room to spare. Buy for the second if the context might grow.
06

See what else that card runs

Every card name links to its own page, which runs the same calculation across the whole model catalogue. Worth a look before buying for Gemma 3 4B alone — a card is usually bought for more than one model.

Answers

Gemma 3 4B — common questions

Can I run Gemma 3 4B on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 5.0 GB and generating roughly 158 tokens per second — a comfortable fit.

Can I run Gemma 3 4B on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 5.0 GB and generating roughly 96.6 tokens per second — a comfortable fit.

Can I run Gemma 3 4B on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 5.0 GB and generating roughly 120 tokens per second — a comfortable fit.

Can I run Gemma 3 4B on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 5.0 GB and generating roughly 142 tokens per second — a comfortable fit.

Is Gemma 3 4B open source?

Its weights are published, so Gemma 3 4B 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 Gemma 3 4B have?

Gemma 3 4B has 4B parameters. Vision Encoder: 417M Embedding Parameters: 675M Non-embedding Parameters: 3,209M. 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 Gemma 3 4B?

Gemma 3 4B was published by Google DeepMind, based in United States of America, categorised as industry.

When was Gemma 3 4B released?

Gemma 3 4B was published in March 2025.

What is Gemma 3 4B used for?

Gemma 3 4B works in Language, Vision, Multimodal, and is recorded as handling language modeling/generation, Question answering, Translation, Chat, Quantitative reasoning, Visual question answering, Code generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Where can I download Gemma 3 4B?

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 Gemma 3 4B?

Around 9.6 × 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 Gemma 3 4B if it does not fit in my GPU?

It can be split between the card and system memory, but Gemma 3 4B generates painfully slowly that way. Nothing on this page assumes offloading.

Would two GPUs run Gemma 3 4B faster?

Capacity adds across cards; throughput does not. Since 818 of the cards we track already hold Gemma 3 4B on their own, a second card is rarely the answer here.

Why does the quantisation differ between cards for Gemma 3 4B?

A larger card holds a more accurate copy. Across the cards that run Gemma 3 4B, 3 compression levels are used; the floor control above pins it to one.

How accurate are these Gemma 3 4B 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 720–1,016 tok/s on the B200 rather than a single number.

What GPU do I need to run Gemma 3 4B?

The smallest card in our catalogue that holds Gemma 3 4B is the Tesla C1080, with 4 GB of memory. It runs the model at Q4_K_M using about 3.2 GB, and produces roughly 21.3 tokens per second. 818 cards in total can run it.

How fast is Gemma 3 4B on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 847 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 779 of the cards that can run Gemma 3 4B clear that.

How much VRAM does Gemma 3 4B need?

About 3.2 GB at Q4_K_M 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.

Source

Original publication

Record last updated 28 November 2025

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