T5-3B TPS calculator

Open weights Google 2.8B parameters October 2019

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 · Q6_K · 19.1 tok/s

Fastest card

B200

1,210 tok/s · 180 GB

Which GPUs can run T5-3B?

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

818 cards match

Calculating

					Needs	Quantisation	Fit
1,210 tok/s 726–1,936 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	3.7 GB	Q8_0	Comfortable
1,210 tok/s 726–1,936 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	3.7 GB	Q8_0	Comfortable
966 tok/s 580–1,546 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	3.7 GB	Q8_0	Comfortable
966 tok/s 580–1,546 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	3.7 GB	Q8_0	Comfortable
773 tok/s 464–1,236 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	3.7 GB	Q8_0	Comfortable
740 tok/s 444–1,183 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	3.7 GB	Q8_0	Comfortable
740 tok/s 444–1,183 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	3.7 GB	Q8_0	Comfortable
708 tok/s 425–1,133 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	3.7 GB	Q8_0	Comfortable
628 tok/s 377–1,005 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	3.7 GB	Q8_0	Comfortable
628 tok/s 377–1,005 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	3.7 GB	Q8_0	Comfortable
628 tok/s 377–1,005 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	3.7 GB	Q8_0	Comfortable
596 tok/s 358–954 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	3.7 GB	Q8_0	Comfortable
508 tok/s 305–813 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	3.7 GB	Q8_0	Comfortable
508 tok/s 305–813 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	3.7 GB	Q8_0	Comfortable
508 tok/s 305–813 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	3.7 GB	Q8_0	Comfortable
508 tok/s 305–813 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	3.7 GB	Q8_0	Comfortable
508 tok/s 305–813 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	3.7 GB	Q8_0	Comfortable
387 tok/s 232–619 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	3.7 GB	Q8_0	Comfortable
387 tok/s 232–619 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	3.7 GB	Q8_0	Comfortable
322 tok/s 193–516 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	3.7 GB	Q8_0	Comfortable
316 tok/s 189–505 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	3.7 GB	Q8_0	Comfortable
309 tok/s 185–494 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	3.7 GB	Q8_0	Comfortable
309 tok/s 185–494 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	3.7 GB	Q8_0	Comfortable
309 tok/s 185–494 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	3.7 GB	Q8_0	Comfortable
309 tok/s 185–494 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	3.7 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
Organisation type: Industry
Country: United States of America
Published: 23 October 2019
Authors: Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu

What it does

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

Domain: Language
Task: Text autocompletion
Approach: Self-supervised learning

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: 2.8B
Training data: 5,100,000,000 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: 9 × 10²¹ FLOP
How it was established: Third-party estimation,Reported

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 v3
Compute cost: $17,421

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 (unrestricted)
Training code: Open source

How it is classified

Labels the source dataset applies when tracking notable models, and how confident it is in the entry.

Frontier model: Yes
Why it is tracked: Highly cited
Record confidence: Confident
Citations: 25,683

Sources

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

Reference: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run T5-3B

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 T5-3B

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

3.0 GB

Fastest

1,210 tok/s

T5-3B is small enough at 2.8B 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, Q6_K compression, roughly 19.1 tokens per second.

The quickest result comes from a B200 at around 1,210 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

Background

T5-3B was published by Google, in United States of America, in October 2019. The organisation is categorised as industry.

It works in Language, and is recorded as doing text autocompletion.

The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached.

Reading the throughput figures

Half the cards that hold it manage more than 38.4 tokens per second, and 784 exceed reading speed outright.

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.

Memory here is estimated from size rather than computed from the architecture, which is not recorded for this model — the numbers are indicative rather than exact.

What went into building it

Training it took roughly 9 × 10²¹ FLOP of computation, on Google TPU v3 — a measure of what producing the model cost, not of how fast it answers.

It was trained on about 5,100,000,000 tokens of text.

The reason it appears in this catalogue at all is highly cited.

Step by step

How to choose a GPU for T5-3B

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

The table lists every card that can hold T5-3B — around 3.0 GB at Q6_K. That figure, not the card's headline performance, is what decides whether it runs.
02

Set the context length you will work at

The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context T5-3B can slip off a card that handles short questions easily.
03

Decide how much compression you will accept

Each card runs the least-compressed copy it can hold — Q6_K on the smallest card that fits. Setting a floor drops the cards that only manage T5-3B by squeezing it further than you would want.
04

Sort by speed

Ranking by tokens per second for T5-3B follows memory bandwidth, not core counts, which is why the B200 tops it at 1,210 tok/s.
05

Read the fit column last

The fit column separates cards that just manage T5-3B from those with room to spare. Buy for the second if the context might grow.
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 T5-3B.

Answers

T5-3B — common questions

Can I run T5-3B on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 3.7 GB and generating roughly 138 tokens per second — a comfortable fit.

Can I run T5-3B on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 3.7 GB and generating roughly 171 tokens per second — a comfortable fit.

Can I run T5-3B on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 3.7 GB and generating roughly 203 tokens per second — a comfortable fit.

Is T5-3B open source?

Its weights are published, so T5-3B 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 T5-3B have?

T5-3B has 2.8B parameters. page 37, 3B and 11B. "To further explore what kind of performance is possible when using larger models, we consider two additional variants. In both cases, we use d_model = 1024, a 24 layer encoder and decoder, and dkv = 128. For the “3B” variant, we use dff = 16,384 with 32-headed attention, which results in around 2.8 billion parameters; for “11B” we use dff = 65,536 with 128-headed attention producing a model with about 11 billion 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 T5-3B?

T5-3B was published by Google, based in United States of America, categorised as industry.

When was T5-3B released?

T5-3B was published in October 2019. 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 T5-3B used for?

T5-3B works in Language, and is recorded as handling text autocompletion. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.

Where can I download T5-3B?

The weights for T5-3B 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 T5-3B?

Around 9 × 10²¹ FLOP, on Google TPU v3. 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 T5-3B if it does not fit in my GPU?

It can be split between the card and system memory, but T5-3B generates painfully slowly that way. Nothing on this page assumes offloading.

Would two GPUs run T5-3B faster?

Capacity adds across cards; throughput does not. Since 818 of the cards we track already hold T5-3B on their own, a second card is rarely the answer here.

Why does the quantisation differ between cards for T5-3B?

A larger card holds a more accurate copy. Across the cards that run T5-3B, 2 compression levels are used; the floor control above pins it to one.

How accurate are these T5-3B speed estimates?

These are estimates with real error bars. The fastest result here, 726–1,936 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 T5-3B?

The smallest card in our catalogue that holds T5-3B is the Tesla C1080, with 4 GB of memory. It runs the model at Q6_K using about 3.0 GB, and produces roughly 19.1 tokens per second. 818 cards in total can run it.

How fast is T5-3B on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 1,210 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 784 of the cards that can run T5-3B clear that.

How much VRAM does T5-3B need?

About 3.0 GB at Q6_K 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 T5-3B on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 3.7 GB and generating roughly 225 tokens per second — a comfortable fit.

Source

Original publication

Record last updated 25 May 2026

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