Gemma 3 27B TPS calculator

Open weights Google DeepMind 27B 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

241 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Xeon Phi 7120P

16 GB · Q3_K_M · 9.7 tok/s

Fastest card

B200

125 tok/s · 180 GB

Which GPUs can run Gemma 3 27B?

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

241 cards match

Calculating

					Needs	Quantisation	Fit
125 tok/s 107–151	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	29.0 GB	Q8_0	Comfortable
125 tok/s 107–151	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	29.0 GB	Q8_0	Comfortable
100 tok/s 60–160 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	29.0 GB	Q8_0	Comfortable
100 tok/s 60–160 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	29.0 GB	Q8_0	Comfortable
80.1 tok/s 48–128 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	29.0 GB	Q8_0	Comfortable
76.7 tok/s 65–92	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	29.0 GB	Q8_0	Comfortable
76.7 tok/s 65–92	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	29.0 GB	Q8_0	Comfortable
73.4 tok/s 44–117 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	29.0 GB	Q8_0	Comfortable
65.2 tok/s 39–104 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	29.0 GB	Q8_0	Comfortable
65.2 tok/s 39–104 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	29.0 GB	Q8_0	Comfortable
65.2 tok/s 39–104 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	29.0 GB	Q8_0	Comfortable
61.8 tok/s 53–74	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	29.0 GB	Q8_0	Comfortable
52.7 tok/s 45–63	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	29.0 GB	Q8_0	Comfortable
52.7 tok/s 45–63	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	29.0 GB	Q8_0	Comfortable
52.7 tok/s 45–63	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	29.0 GB	Q8_0	Comfortable
52.7 tok/s 45–63	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	29.0 GB	Q8_0	Comfortable
52.7 tok/s 45–63	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	29.0 GB	Q8_0	Comfortable
47.8 tok/s 41–57	Tesla V100 SXM2 16 GB NVIDIA	16 GB	1,130 GB/s	Nov 2019	13.3 GB	Q3_K_M	Tight
42.6 tok/s 36–51	DRIVE A100 PROD NVIDIA	32 GB	1,870 GB/s	May 2020	22.7 GB	Q6_K	Comfortable
42.6 tok/s 36–51	GRID A100A NVIDIA	32 GB	1,870 GB/s	May 2020	22.7 GB	Q6_K	Comfortable
40.8 tok/s 35–49	GeForce RTX 5090 NVIDIA	32 GB	1,790 GB/s	Jan 2025	22.7 GB	Q6_K	Comfortable
40.8 tok/s 35–49	GeForce RTX 5090 D NVIDIA	32 GB	1,790 GB/s	Jan 2025	22.7 GB	Q6_K	Comfortable
40.6 tok/s 35–49	GeForce RTX 5080 NVIDIA	16 GB	960 GB/s	Jan 2025	13.3 GB	Q3_K_M	Tight
40.1 tok/s 24–64 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	29.0 GB	Q8_0	Comfortable
40.1 tok/s 24–64 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	29.0 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: 27B
Training data: 14,000,000,000,000 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: 2.3 × 10²⁴ 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 v5p
Chips used: 6,144
Power draw: 6.5 MW

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 that run Gemma 3 27B

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 27B

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

Xeon Phi 7120P

Memory needed

13.3 GB

Fastest

125 tok/s

With 27B parameters, Gemma 3 27B lands in the range a serious desktop card can handle once the weights are compressed. 241 of the cards we track can run it.

The smallest card that holds it is the Xeon Phi 7120P with 16 GB, running it at Q3_K_M and producing around 9.7 tokens per second.

Top of the range is the B200, at roughly 125 tokens per second thanks to 8,000 GB/s of bandwidth.

About this model

Gemma 3 27B was published by Google DeepMind, in United States of America, in March 2025. The organisation is categorised as 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.

It is derived from SigLIP 400M rather than trained from scratch, which is the usual way a specialised model is produced.

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. It is published under the google organisation on Hugging Face.

How fast it runs, and why

Across every card that can run it, the middle of the range is about 19.0 tokens per second, and 196 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 attention layout is on file, so the memory figures are computed exactly rather than approximated.

How it was trained

The training run consumed about 2.3 × 10²⁴ FLOP, on Google TPU v5p. That figure describes the cost of creating it and has no bearing on how quickly it generates text.

The training set ran to roughly 14,000,000,000,000 tokens.

Step by step

How to choose a GPU for Gemma 3 27B

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

Every card here has been checked against Gemma 3 27B — around 13.3 GB at Q3_K_M. Capacity is the gate — a card either holds it or it does not.
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 Gemma 3 27B 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 27B — Q3_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 27B is effectively an ordering by memory bandwidth, which is why the B200 tops it at 125 tok/s.
05

Read the fit column last

A tight fit runs Gemma 3 27B but leaves nothing spare for a longer conversation; comfortable has headroom. If you expect to grow the context, buy for comfortable.
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 27B alone — a card is usually bought for more than one model.

Answers

Gemma 3 27B — common questions

When was Gemma 3 27B released?

Gemma 3 27B was published in March 2025.

What is Gemma 3 27B used for?

Gemma 3 27B 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 27B?

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 27B?

Around 2.3 × 10²⁴ FLOP, on Google TPU v5p. 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 27B if it does not fit in my GPU?

It can be split between the card and system memory, but Gemma 3 27B generates painfully slowly that way — the nearest miss we calculate is short by 5.6 GB. Nothing on this page assumes offloading.

Would two GPUs run Gemma 3 27B faster?

A second card roughly doubles the memory available but not the generation rate. With 241 cards already able to run Gemma 3 27B alone, the case for pairing is weak.

Why does the quantisation differ between cards for Gemma 3 27B?

A larger card holds a more accurate copy. Across the cards that run Gemma 3 27B, 5 compression levels are used; the floor control above pins it to one.

How accurate are these Gemma 3 27B speed estimates?

These are estimates with real error bars. The fastest result here, 107–151 tok/s on the B200, could reasonably land anywhere in its published range depending on which runtime you use.

What GPU do I need to run Gemma 3 27B?

The smallest card in our catalogue that holds Gemma 3 27B is the Xeon Phi 7120P, with 16 GB of memory. It runs the model at Q3_K_M using about 13.3 GB, and produces roughly 9.7 tokens per second. 241 cards in total can run it.

How fast is Gemma 3 27B on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 125 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 196 of the cards that can run Gemma 3 27B clear that.

How much VRAM does Gemma 3 27B need?

About 13.3 GB at Q3_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.

Can I run Gemma 3 27B on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q3_K_M, using about 13.3 GB and generating roughly 47.8 tokens per second — a tight fit.

Can I run Gemma 3 27B on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q5_K_M, using about 19.5 GB and generating roughly 37.5 tokens per second — a tight fit.

Is Gemma 3 27B open source?

Its weights are published, so Gemma 3 27B 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 27B have?

Gemma 3 27B has 27B parameters. Vision Encoder: 417M Embedding Parameters: 1,416M Non-embedding Parameters: 25,600M. 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 27B?

Gemma 3 27B 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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