EmbeddingGemma TPS calculator

Open weights Google DeepMind 308M parameters September 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 · Q8_0 · 120 tok/s

Fastest card

B200

11,001 tok/s · 180 GB

Which GPUs can run EmbeddingGemma?

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
11,001 tok/s 6,600–17,601 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	1.0 GB	Q8_0	Comfortable
11,001 tok/s 6,600–17,601 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	1.0 GB	Q8_0	Comfortable
8,784 tok/s 5,271–14,055 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	1.0 GB	Q8_0	Comfortable
8,784 tok/s 5,271–14,055 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	1.0 GB	Q8_0	Comfortable
7,025 tok/s 4,215–11,241 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	1.0 GB	Q8_0	Comfortable
6,724 tok/s 4,035–10,759 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.0 GB	Q8_0	Comfortable
6,724 tok/s 4,035–10,759 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	1.0 GB	Q8_0	Comfortable
6,435 tok/s 3,861–10,297 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	1.0 GB	Q8_0	Comfortable
5,711 tok/s 3,427–9,138 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	1.0 GB	Q8_0	Comfortable
5,711 tok/s 3,427–9,138 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	1.0 GB	Q8_0	Comfortable
5,711 tok/s 3,427–9,138 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	1.0 GB	Q8_0	Comfortable
5,418 tok/s 3,251–8,669 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	1.0 GB	Q8_0	Comfortable
4,620 tok/s 2,772–7,393 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.0 GB	Q8_0	Comfortable
4,620 tok/s 2,772–7,393 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	1.0 GB	Q8_0	Comfortable
4,620 tok/s 2,772–7,393 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	1.0 GB	Q8_0	Comfortable
4,620 tok/s 2,772–7,393 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	1.0 GB	Q8_0	Comfortable
4,620 tok/s 2,772–7,393 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	1.0 GB	Q8_0	Comfortable
3,518 tok/s 2,111–5,629 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	1.0 GB	Q8_0	Comfortable
3,518 tok/s 2,111–5,629 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	1.0 GB	Q8_0	Comfortable
2,932 tok/s 1,759–4,691 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	1.0 GB	Q8_0	Comfortable
2,869 tok/s 1,721–4,591 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	1.0 GB	Q8_0	Comfortable
2,805 tok/s 1,683–4,488 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	1.0 GB	Q8_0	Comfortable
2,805 tok/s 1,683–4,488 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	1.0 GB	Q8_0	Comfortable
2,805 tok/s 1,683–4,488 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	1.0 GB	Q8_0	Comfortable
2,805 tok/s 1,683–4,488 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	1.0 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: 5 September 2025

What it does

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

Domain: Language
Task: Semantic embedding
Base model: Gemma 3 270M

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: 308M
Training data: 320,000,000,000 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.

Fine-tuning compute: 5.9 × 10²⁰ FLOP

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

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.

Record confidence: Confident

Sources

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

Reference: EmbeddingGemma is a 300M parameter embedding model from Google.
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run EmbeddingGemma

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 EmbeddingGemma

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

Hardware requirements in practice

Minimum card

Tesla C1080

Memory needed

1.0 GB

Fastest

11,001 tok/s

EmbeddingGemma is small enough at 308M parameters that hardware is rarely the obstacle — 818 of the cards we track can run it, including cards several years old.

The entry point is the Tesla C1080: 4 GB of memory, Q8_0 compression, roughly 120 tokens per second.

Top of the range is the B200, at roughly 11,001 tokens per second thanks to 8,000 GB/s of bandwidth.

About this model

EmbeddingGemma was published by Google DeepMind, in United States of America, in September 2025. It comes out of industry.

It works in Language, and is recorded as doing semantic embedding.

Its starting point was Gemma 3 270M — most models at this scale are adapted from an existing base rather than built from nothing.

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.

How fast it runs, and why

The median result is around 308.9 tokens per second; 818 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.

Without the attention layout on record, the memory column is an approximation. It is close enough to choose hardware by, and least reliable at long context.

What went into building it

The training set ran to roughly 320,000,000,000 tokens.

Step by step

How to choose a GPU for EmbeddingGemma

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 EmbeddingGemma — around 1.0 GB at Q8_0. Capacity is the gate — a card either holds it or it does not.
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 EmbeddingGemma can slip off a card that handles short questions easily.
03

Decide how much compression you will accept

Compression is what makes EmbeddingGemma fit smaller cards, at some cost in accuracy — Q8_0 on the smallest card that fits. A minimum quality removes the ones that go too far.
04

Rank by throughput rather than spec sheet

Sort by speed to see how cards rank for EmbeddingGemma. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 11,001 tok/s.
05

Look at the headroom, not just the fit

Tight means EmbeddingGemma 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

See what else that card runs

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

Answers

EmbeddingGemma — common questions

Can I run EmbeddingGemma on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 1.0 GB and generating roughly 1,255 tokens per second — a comfortable fit.

Can I run EmbeddingGemma on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 1.0 GB and generating roughly 1,554 tokens per second — a comfortable fit.

Can I run EmbeddingGemma on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 1.0 GB and generating roughly 1,843 tokens per second — a comfortable fit.

Is EmbeddingGemma open source?

Its weights are published, so EmbeddingGemma 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 EmbeddingGemma have?

EmbeddingGemma has 308M parameters. 300M. 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 EmbeddingGemma?

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

When was EmbeddingGemma released?

EmbeddingGemma was published in September 2025.

What is EmbeddingGemma used for?

EmbeddingGemma works in Language, and is recorded as handling semantic embedding. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download EmbeddingGemma?

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 EmbeddingGemma 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 EmbeddingGemma is rarely worth using. Every figure here assumes the whole model is on the card.

Would two GPUs run EmbeddingGemma faster?

Two cards buy memory rather than speed. That matters for EmbeddingGemma only if one card cannot hold it — 818 can, so a second adds little.

Why does the quantisation differ between cards for EmbeddingGemma?

A larger card holds a more accurate copy. Across the cards that run EmbeddingGemma, 1 compression levels are used; the floor control above pins it to one.

How accurate are these EmbeddingGemma speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 6,600–17,601 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 EmbeddingGemma?

The smallest card in our catalogue that holds EmbeddingGemma is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 1.0 GB, and produces roughly 120 tokens per second. 818 cards in total can run it.

How fast is EmbeddingGemma on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 11,001 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 818 of the cards that can run EmbeddingGemma clear that.

How much VRAM does EmbeddingGemma need?

About 1.0 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 EmbeddingGemma on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 1.0 GB and generating roughly 2,049 tokens per second — a comfortable fit.

Source

Original publication

Record last updated 28 November 2025

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