Gemma 2B TPS calculator

Open weights Google DeepMind 2.5B parameters February 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

818 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Tesla C1080

4 GB · Q8_0 · 14.7 tok/s

Fastest card

B200

1,352 tok/s · 180 GB

Which GPUs can run Gemma 2B?

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
1,352 tok/s 1,149–1,622	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	3.4 GB	Q8_0	Comfortable
1,352 tok/s 1,149–1,622	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	3.4 GB	Q8_0	Comfortable
1,079 tok/s 648–1,727 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	3.4 GB	Q8_0	Comfortable
1,079 tok/s 648–1,727 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	3.4 GB	Q8_0	Comfortable
863 tok/s 518–1,381 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	3.4 GB	Q8_0	Comfortable
826 tok/s 702–992	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	3.4 GB	Q8_0	Comfortable
826 tok/s 702–992	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	3.4 GB	Q8_0	Comfortable
791 tok/s 474–1,265 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	3.4 GB	Q8_0	Comfortable
702 tok/s 421–1,123 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	3.4 GB	Q8_0	Comfortable
702 tok/s 421–1,123 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	3.4 GB	Q8_0	Comfortable
702 tok/s 421–1,123 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	3.4 GB	Q8_0	Comfortable
666 tok/s 566–799	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	3.4 GB	Q8_0	Comfortable
568 tok/s 483–681	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	3.4 GB	Q8_0	Comfortable
568 tok/s 483–681	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	3.4 GB	Q8_0	Comfortable
568 tok/s 483–681	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	3.4 GB	Q8_0	Comfortable
568 tok/s 483–681	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	3.4 GB	Q8_0	Comfortable
568 tok/s 483–681	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	3.4 GB	Q8_0	Comfortable
432 tok/s 259–692 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	3.4 GB	Q8_0	Comfortable
432 tok/s 259–692 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	3.4 GB	Q8_0	Comfortable
360 tok/s 216–576 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	3.4 GB	Q8_0	Comfortable
353 tok/s 212–564 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	3.4 GB	Q8_0	Comfortable
345 tok/s 293–414	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	3.4 GB	Q8_0	Comfortable
345 tok/s 293–414	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	3.4 GB	Q8_0	Comfortable
345 tok/s 293–414	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	3.4 GB	Q8_0	Comfortable
345 tok/s 293–414	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	3.4 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: 21 February 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: 2.5B
Training data: 3,000,000,000,000 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: 4.5 × 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: 512
Power draw: 228.0 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

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: Gemma: Open Models Based on Gemini Research and Technology
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Gemma 2B

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 2B

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

3.4 GB

Fastest

1,352 tok/s

Gemma 2B is small enough at 2.5B 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 Q8_0 and producing around 14.7 tokens per second.

At the other end, a B200 generates roughly 1,352 tokens per second on it, on the strength of 8,000 GB/s of memory bandwidth.

What this model is

Gemma 2B was published by Google DeepMind, in United States of America, in February 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.

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.

What decides the speed

Across every card that can run it, the middle of the range is about 38.0 tokens per second, and 783 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.

What went into building it

The training run consumed about 4.5 × 10²² FLOP, on Google TPU v5e. That figure describes the cost of creating it and has no bearing on how quickly it generates text.

Around 3,000,000,000,000 tokens went into training it.

Step by step

How to choose a GPU for Gemma 2B

The table above has already assessed every card we hold specifications for against this model. Getting to your answer takes six steps.

01

Read the memory figure first

Look at what Gemma 2B actually needs — around 3.4 GB at Q8_0. No amount of processing power compensates for a card that cannot hold it.
02

Decide how long your conversations run

The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context Gemma 2B can slip off a card that handles short questions easily.
03

Decide how much compression you will accept

The quantisation column varies by card, because a bigger card holds a more accurate copy of Gemma 2B — Q8_0 on the smallest card that fits. Set a floor to hold the comparison at one level.
04

Compare tokens per second, not specifications

Sort by speed to see how cards rank for Gemma 2B. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 1,352 tok/s.
05

Look at the headroom, not just the fit

Tight means Gemma 2B loads and works, with no room to raise the context later. Comfortable means you can. The difference matters more than a few tokens per second.
06

Check the card from the other side

Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond Gemma 2B.

Answers

Gemma 2B — common questions

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

Would two GPUs run Gemma 2B faster?

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

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

Each card is shown running the least-compressed copy it can hold, and Gemma 2B appears at 1 different compression levels across the cards that fit it. Bigger cards get the more accurate version.

How accurate are these Gemma 2B speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 1,149–1,622 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 Gemma 2B?

The smallest card in our catalogue that holds Gemma 2B is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 3.4 GB, and produces roughly 14.7 tokens per second. 818 cards in total can run it.

How fast is Gemma 2B on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 1,352 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 Gemma 2B clear that.

How much VRAM does Gemma 2B need?

About 3.4 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 Gemma 2B on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 3.4 GB and generating roughly 252 tokens per second — a comfortable fit.

Can I run Gemma 2B on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 3.4 GB and generating roughly 154 tokens per second — a comfortable fit.

Can I run Gemma 2B on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 3.4 GB and generating roughly 191 tokens per second — a comfortable fit.

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

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 3.4 GB and generating roughly 226 tokens per second — a comfortable fit.

Is Gemma 2B open source?

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

Gemma 2B has 2.5B parameters. Table 2, sum of embedding and non-embedding parameters: 2B 524,550,144 + 1,981,884,416 = 2506434560. 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 2B?

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

When was Gemma 2B released?

Gemma 2B was published in February 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.

What is Gemma 2B used for?

Gemma 2B 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 2B?

The weights for Gemma 2B are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.

How much compute was used to train Gemma 2B?

Around 4.5 × 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.

Source

Original publication

Record last updated 28 November 2025

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