Gemma 2 27B TPS calculator

Open weights Google DeepMind 27B parameters June 2024

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 2 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	30.1 GB	Q8_0	Comfortable
125 tok/s 107–151	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	30.1 GB	Q8_0	Comfortable
100 tok/s 60–160 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	30.1 GB	Q8_0	Comfortable
100 tok/s 60–160 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	30.1 GB	Q8_0	Comfortable
80.1 tok/s 48–128 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	30.1 GB	Q8_0	Comfortable
76.7 tok/s 65–92	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	30.1 GB	Q8_0	Comfortable
76.7 tok/s 65–92	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	30.1 GB	Q8_0	Comfortable
73.4 tok/s 44–117 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	30.1 GB	Q8_0	Comfortable
65.2 tok/s 39–104 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	30.1 GB	Q8_0	Comfortable
65.2 tok/s 39–104 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	30.1 GB	Q8_0	Comfortable
65.2 tok/s 39–104 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	30.1 GB	Q8_0	Comfortable
61.8 tok/s 53–74	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	30.1 GB	Q8_0	Comfortable
52.7 tok/s 45–63	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	30.1 GB	Q8_0	Comfortable
52.7 tok/s 45–63	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	30.1 GB	Q8_0	Comfortable
52.7 tok/s 45–63	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	30.1 GB	Q8_0	Comfortable
52.7 tok/s 45–63	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	30.1 GB	Q8_0	Comfortable
52.7 tok/s 45–63	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	30.1 GB	Q8_0	Comfortable
47.8 tok/s 41–57	Tesla V100 SXM2 16 GB NVIDIA	16 GB	1,130 GB/s	Nov 2019	14.4 GB	Q3_K_M	Tight
42.6 tok/s 36–51	DRIVE A100 PROD NVIDIA	32 GB	1,870 GB/s	May 2020	23.8 GB	Q6_K	Comfortable
42.6 tok/s 36–51	GRID A100A NVIDIA	32 GB	1,870 GB/s	May 2020	23.8 GB	Q6_K	Comfortable
40.8 tok/s 35–49	GeForce RTX 5090 NVIDIA	32 GB	1,790 GB/s	Jan 2025	23.8 GB	Q6_K	Comfortable
40.8 tok/s 35–49	GeForce RTX 5090 D NVIDIA	32 GB	1,790 GB/s	Jan 2025	23.8 GB	Q6_K	Comfortable
40.6 tok/s 35–49	GeForce RTX 5080 NVIDIA	16 GB	960 GB/s	Jan 2025	14.4 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	30.1 GB	Q8_0	Comfortable
40.1 tok/s 24–64 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	30.1 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: 24 June 2024
Authors: Gemma Team, Google DeepMind

What it does

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

Domain: Language
Task: Language modeling/generation, Chat, Code generation, Question answering, Quantitative reasoning

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: 13,000,000,000,000 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: 2.1 × 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 2 offers best-in-class performance, runs at incredible speed across different hardware and easily integrates with other AI tools.
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Gemma 2 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 2 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 you need to run it

Minimum card

Xeon Phi 7120P

Memory needed

14.4 GB

Fastest

125 tok/s

With 27B parameters, Gemma 2 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 least hardware that works is a Xeon Phi 7120P. Its 16 GB is enough at Q3_K_M compression, giving roughly 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.

What this model is

Gemma 2 27B was published by Google DeepMind, in United States of America, in June 2024. It comes out of industry.

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

Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded. It is published under the google organisation on Hugging Face.

What decides the speed

Half the cards that hold it manage more than 19.0 tokens per second, and 196 exceed reading speed outright.

Every weight participates in every token here, so bandwidth is the whole story: the ranking below is effectively a ranking of memory throughput.

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

Training and provenance

The training run consumed about 2.1 × 10²⁴ FLOP, on Google TPU v5p. That figure describes the cost of creating it and has no bearing on how quickly it generates text.

It was trained on about 13,000,000,000,000 tokens of text.

Step by step

How to choose a GPU for Gemma 2 27B

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

Look at what Gemma 2 27B actually needs — around 14.4 GB at Q3_K_M. 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 Gemma 2 27B.
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 2 27B — Q3_K_M on the smallest card that fits. Set a floor to hold the comparison at one level.
04

Rank by throughput rather than spec sheet

Sort by speed to see how cards rank for Gemma 2 27B. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 125 tok/s.
05

Look at the headroom, not just the fit

A tight fit runs Gemma 2 27B but leaves nothing spare for a longer conversation; comfortable has headroom. If you expect to grow the context, buy for comfortable.
06

Check the card from the other side

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 2 27B alone — a card is usually bought for more than one model.

Answers

Gemma 2 27B — common questions

What is Gemma 2 27B used for?

Gemma 2 27B works in Language, and is recorded as handling language modeling/generation, Chat, Code generation, Question answering, Quantitative reasoning. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download Gemma 2 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 2 27B?

Around 2.1 × 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 2 27B 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 Gemma 2 27B is rarely worth using — the nearest miss we calculate is short by 6.7 GB. Every figure here assumes the whole model is on the card.

Would two GPUs run Gemma 2 27B faster?

A second card roughly doubles the memory available but not the generation rate. With 241 cards already able to run Gemma 2 27B alone, the case for pairing is weak.

Why does the quantisation differ between cards for Gemma 2 27B?

A larger card holds a more accurate copy. Across the cards that run Gemma 2 27B, 5 compression levels are used; the floor control above pins it to one.

How accurate are these Gemma 2 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 2 27B?

The smallest card in our catalogue that holds Gemma 2 27B is the Xeon Phi 7120P, with 16 GB of memory. It runs the model at Q3_K_M using about 14.4 GB, and produces roughly 9.7 tokens per second. 241 cards in total can run it.

How fast is Gemma 2 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 2 27B clear that.

How much VRAM does Gemma 2 27B need?

About 14.4 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 2 27B on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q3_K_M, using about 14.4 GB and generating roughly 47.8 tokens per second — a tight fit.

Can I run Gemma 2 27B on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q5_K_M, using about 20.6 GB and generating roughly 37.5 tokens per second — a tight fit.

Is Gemma 2 27B open source?

Its weights are published, so Gemma 2 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 2 27B have?

Gemma 2 27B has 27B parameters. 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 2 27B?

Gemma 2 27B was published by Google DeepMind, based in United States of America, categorised as industry.

When was Gemma 2 27B released?

Gemma 2 27B was published in June 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.

Source

Original publication

Record last updated 28 November 2025

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