TxGemma 9B TPS calculator

Open weights Google DeepMind,Google Research 9B parameters April 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

509 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Xeon Phi 5110P

8 GB · IQ4_XS · 24.0 tok/s

Fastest card

B200

376 tok/s · 180 GB

Which GPUs can run TxGemma 9B?

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

509 cards match

Calculating

					Needs	Quantisation	Fit
376 tok/s 320–452	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	11.7 GB	Q8_0	Comfortable
376 tok/s 320–452	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	11.7 GB	Q8_0	Comfortable
301 tok/s 180–481 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	11.7 GB	Q8_0	Comfortable
301 tok/s 180–481 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	11.7 GB	Q8_0	Comfortable
240 tok/s 144–385 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	11.7 GB	Q8_0	Comfortable
230 tok/s 196–276	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	11.7 GB	Q8_0	Comfortable
230 tok/s 196–276	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	11.7 GB	Q8_0	Comfortable
220 tok/s 132–352 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	11.7 GB	Q8_0	Comfortable
195 tok/s 117–313 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	11.7 GB	Q8_0	Comfortable
195 tok/s 117–313 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	11.7 GB	Q8_0	Comfortable
195 tok/s 117–313 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	11.7 GB	Q8_0	Comfortable
185 tok/s 158–222	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	11.7 GB	Q8_0	Comfortable
172 tok/s 146–207	CMP 170HX 8 GB NVIDIA	8 GB	1,490 GB/s	Sep 2021	7.0 GB	IQ4_XS	Tight
158 tok/s 134–190	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	11.7 GB	Q8_0	Comfortable
158 tok/s 134–190	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	11.7 GB	Q8_0	Comfortable
158 tok/s 134–190	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	11.7 GB	Q8_0	Comfortable
158 tok/s 134–190	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	11.7 GB	Q8_0	Comfortable
158 tok/s 134–190	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	11.7 GB	Q8_0	Comfortable
131 tok/s 111–157	CMP 170HX 10 GB NVIDIA	10 GB	1,560 GB/s	Sep 2021	8.6 GB	Q5_K_M	Tight
120 tok/s 72–193 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	11.7 GB	Q8_0	Comfortable
120 tok/s 72–193 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	11.7 GB	Q8_0	Comfortable
100 tok/s 60–161 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	11.7 GB	Q8_0	Comfortable
98.2 tok/s 59–157 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	11.7 GB	Q8_0	Comfortable
96.0 tok/s 82–115	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	11.7 GB	Q8_0	Comfortable
96.0 tok/s 82–115	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	11.7 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,Google Research
Organisation type: Industry,Industry
Country: United States of America
Published: 8 April 2025
Authors: Eric Wang, Samuel Schmidgall, Paul F. Jaeger, Fan Zhang, Rory Pilgrim, Yossi Matias, Joelle Barral, David Fleet, Shekoofeh Azizi

What it does

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

Domain: Language, Biology
Task: Protein or nucleotide language model (pLM/nLM), Protein property prediction, Small molecule property prediction, Chat, Question answering, Protein question answering, Protein function prediction, Language modeling/generation
Base model: Gemma 2 9B

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: 9B
Training data: tokens
Epochs: 12

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: 4.4 × 10²³ FLOP
How it was established: Operation counting
Fine-tuning compute: 3.6 × 10²¹ 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 v4
Chips used: 256
Power draw: 170.7 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: TxGemma: Efficient and Agentic LLMs for Therapeutics
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run TxGemma 9B

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 TxGemma 9B

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 5110P

Memory needed

7.0 GB

Fastest

376 tok/s

TxGemma 9B is small enough at 9B parameters that hardware is rarely the obstacle — 509 of the cards we track can run it, including cards several years old.

The least hardware that works is a Xeon Phi 5110P. Its 8 GB is enough at IQ4_XS compression, giving roughly 24.0 tokens per second.

Top of the range is the B200, at roughly 376 tokens per second thanks to 8,000 GB/s of bandwidth.

About this model

TxGemma 9B was published by Google DeepMind,Google Research, in United States of America, in April 2025. The organisation is categorised as industry,Industry.

It works in Language, Biology, and is recorded as doing protein or nucleotide language model (pLM/nLM), Protein property prediction, Small molecule property prediction, Chat, Question answering, Protein question answering, Protein function prediction, Language modeling/generation.

Its starting point was Gemma 2 9B — 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 26.0 tokens per second; 473 cards produce text faster than most people read it.

It is a dense model, so every parameter is read for every token produced. That makes speed track memory bandwidth almost exactly — a card with twice the bandwidth generates roughly twice as fast.

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

Training and provenance

The training run consumed about 4.4 × 10²³ FLOP, on Google TPU v4. That figure describes the cost of creating it and has no bearing on how quickly it generates text.

Step by step

How to choose a GPU for TxGemma 9B

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 TxGemma 9B — around 7.0 GB at IQ4_XS. That figure, not the card's headline performance, is what decides whether it runs.
02

Match the context to your actual use

Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason TxGemma 9B stops fitting a card that seemed fine.
03

Decide how much compression you will accept

Compression is what makes TxGemma 9B fit smaller cards, at some cost in accuracy — IQ4_XS on the smallest card that fits. A minimum quality removes the ones that go too far.
04

Sort by speed

Sort by speed to see how cards rank for TxGemma 9B. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 376 tok/s.
05

Look at the headroom, not just the fit

A tight fit runs TxGemma 9B but leaves nothing spare for a longer conversation; comfortable has headroom. If you expect to grow the context, buy for comfortable.
06

Open the card you have settled on

Every card name links to its own page, which runs the same calculation across the whole model catalogue. Worth a look before buying for TxGemma 9B alone — a card is usually bought for more than one model.

Answers

TxGemma 9B — common questions

How much compute was used to train TxGemma 9B?

Around 4.4 × 10²³ FLOP, on Google TPU v4. 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 TxGemma 9B 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 TxGemma 9B is rarely worth using — the nearest miss we calculate is short by 2.2 GB. Every figure here assumes the whole model is on the card.

Would two GPUs run TxGemma 9B faster?

Two cards buy memory rather than speed. That matters for TxGemma 9B only if one card cannot hold it — 509 can, so a second adds little.

Why does the quantisation differ between cards for TxGemma 9B?

Each card is shown running the least-compressed copy it can hold, and TxGemma 9B appears at 4 different compression levels across the cards that fit it. Bigger cards get the more accurate version.

How accurate are these TxGemma 9B 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 320–452 tok/s on the B200 rather than a single number.

What GPU do I need to run TxGemma 9B?

The smallest card in our catalogue that holds TxGemma 9B is the Xeon Phi 5110P, with 8 GB of memory. It runs the model at IQ4_XS using about 7.0 GB, and produces roughly 24.0 tokens per second. 509 cards in total can run it.

How fast is TxGemma 9B on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 376 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 473 of the cards that can run TxGemma 9B clear that.

How much VRAM does TxGemma 9B need?

About 7.0 GB at IQ4_XS 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 TxGemma 9B on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at IQ4_XS, using about 7.0 GB and generating roughly 172 tokens per second — a tight fit.

Can I run TxGemma 9B on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q6_K, using about 9.6 GB and generating roughly 62.4 tokens per second — a tight fit.

Can I run TxGemma 9B on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 11.7 GB and generating roughly 53.2 tokens per second — a comfortable fit.

Can I run TxGemma 9B on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 11.7 GB and generating roughly 63.1 tokens per second — a comfortable fit.

Is TxGemma 9B open source?

Its weights are published, so TxGemma 9B 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 TxGemma 9B have?

TxGemma 9B has 9B parameters. 9B. 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 TxGemma 9B?

TxGemma 9B was published by Google DeepMind,Google Research, based in United States of America, categorised as industry,Industry.

When was TxGemma 9B released?

TxGemma 9B was published in April 2025.

What is TxGemma 9B used for?

TxGemma 9B works in Language, Biology, and is recorded as handling protein or nucleotide language model (pLM/nLM), Protein property prediction, Small molecule property prediction, Chat, Question answering, Protein question answering, Protein function prediction, Language modeling/generation. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download TxGemma 9B?

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.

Source

Original publication

Record last updated 28 November 2025

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