RT-1 TPS calculator

Open weights Google 35M parameters December 2022

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 · 1,053 tok/s

Fastest card

B200

96,807 tok/s · 180 GB

Which GPUs can run RT-1?

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
96,807 tok/s 58,084–154,891 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	0.7 GB	Q8_0	Comfortable
96,807 tok/s 58,084–154,891 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	0.7 GB	Q8_0	Comfortable
77,303 tok/s 46,382–123,684 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	0.7 GB	Q8_0	Comfortable
77,303 tok/s 46,382–123,684 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	0.7 GB	Q8_0	Comfortable
61,823 tok/s 37,094–98,917 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	0.7 GB	Q8_0	Comfortable
59,173 tok/s 35,504–94,677 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	0.7 GB	Q8_0	Comfortable
59,173 tok/s 35,504–94,677 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	0.7 GB	Q8_0	Comfortable
56,632 tok/s 33,979–90,611 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	0.7 GB	Q8_0	Comfortable
50,261 tok/s 30,157–80,417 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	0.7 GB	Q8_0	Comfortable
50,261 tok/s 30,157–80,417 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	0.7 GB	Q8_0	Comfortable
50,261 tok/s 30,157–80,417 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	0.7 GB	Q8_0	Comfortable
47,677 tok/s 28,606–76,284 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	0.7 GB	Q8_0	Comfortable
40,659 tok/s 24,395–65,054 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	0.7 GB	Q8_0	Comfortable
40,659 tok/s 24,395–65,054 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	0.7 GB	Q8_0	Comfortable
40,659 tok/s 24,395–65,054 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	0.7 GB	Q8_0	Comfortable
40,659 tok/s 24,395–65,054 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	0.7 GB	Q8_0	Comfortable
40,659 tok/s 24,395–65,054 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	0.7 GB	Q8_0	Comfortable
30,959 tok/s 18,575–49,534 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	0.7 GB	Q8_0	Comfortable
30,959 tok/s 18,575–49,534 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	0.7 GB	Q8_0	Comfortable
25,799 tok/s 15,479–41,278 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	0.7 GB	Q8_0	Comfortable
25,248 tok/s 15,149–40,397 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	0.7 GB	Q8_0	Comfortable
24,686 tok/s 14,811–39,497 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	0.7 GB	Q8_0	Comfortable
24,686 tok/s 14,811–39,497 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	0.7 GB	Q8_0	Comfortable
24,686 tok/s 14,811–39,497 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	0.7 GB	Q8_0	Comfortable
24,686 tok/s 14,811–39,497 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	0.7 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: 13 December 2022
Authors: Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Tomas Jackson, Sally Jesmonth, Nikhil J Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Isabel Leal, Kuang-Huei Lee, Sergey Levine, Yao Lu, Utsav Malla, Deeksha Manjunath, Igor Mordatch, Ofir Nach…

What it does

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

Domain: Robotics
Task: Robotic manipulation
Approach: Reinforcement 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: 35M
Training data: tokens

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.

Why it is tracked: SOTA improvement
Record confidence: Confident
Citations: 2,174

Sources

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

Reference: RT-1: Robotics Transformer for Real-World Control at Scale
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run RT-1

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 RT-1

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

Tesla C1080

Memory needed

0.7 GB

Fastest

96,807 tok/s

RT-1 is small enough at 35M parameters that hardware is rarely the obstacle — 818 of the cards we track can run it, including cards several years old.

The least hardware that works is a Tesla C1080. Its 4 GB is enough at Q8_0 compression, giving roughly 1,053 tokens per second.

At the other end, a B200 generates roughly 96,807 tokens per second on it, on the strength of 8,000 GB/s of memory bandwidth.

About this model

RT-1 was published by Google, in United States of America, in December 2022. The organisation is categorised as industry.

It works in Robotics, and is recorded as doing robotic manipulation.

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

How fast it runs, and why

Across every card that can run it, the middle of the range is about 2,718.3 tokens per second, and 818 of them clear the ten tokens per second that roughly matches reading speed.

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

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

Its inclusion criterion is sOTA improvement.

Step by step

How to choose a GPU for RT-1

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

The table lists every card that can hold RT-1 — around 0.7 GB at Q8_0. 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 RT-1 can slip off a card that handles short questions easily.
03

Choose how far you will compress it

Compression is what makes RT-1 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

The speed ordering for RT-1 is effectively an ordering by memory bandwidth, which is why the B200 tops it at 96,807 tok/s.
05

Check the fit verdict before buying

Tight means RT-1 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

Every card name links to its own page, which runs the same calculation across the whole model catalogue. Worth a look before buying for RT-1 alone — a card is usually bought for more than one model.

Answers

RT-1 — common questions

Where can I download RT-1?

The weights for RT-1 are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.

Can I run RT-1 if it does not fit in my GPU?

It can be split between the card and system memory, but RT-1 generates painfully slowly that way. Nothing on this page assumes offloading.

Would two GPUs run RT-1 faster?

Two cards buy memory rather than speed. That matters for RT-1 only if one card cannot hold it — 818 can, so a second adds little.

Why does the quantisation differ between cards for RT-1?

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

How accurate are these RT-1 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 58,084–154,891 tok/s on the B200 rather than a single number.

What GPU do I need to run RT-1?

The smallest card in our catalogue that holds RT-1 is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 0.7 GB, and produces roughly 1,053 tokens per second. 818 cards in total can run it.

How fast is RT-1 on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 96,807 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 RT-1 clear that.

How much VRAM does RT-1 need?

About 0.7 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 RT-1 on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 0.7 GB and generating roughly 18,030 tokens per second — a comfortable fit.

Can I run RT-1 on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 0.7 GB and generating roughly 11,041 tokens per second — a comfortable fit.

Can I run RT-1 on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 0.7 GB and generating roughly 13,674 tokens per second — a comfortable fit.

Can I run RT-1 on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 0.7 GB and generating roughly 16,215 tokens per second — a comfortable fit.

Is RT-1 open source?

Its weights are published, so RT-1 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 RT-1 have?

RT-1 has 35M parameters. "we also limit the size of the model compared to the original publication, which was 1.2B parameters (resulting in on robot inference time of 1.9s), to be of similar size to RT-1 (37M parameters for Gato vs. 35M for RT-1" 16M params for image tokenizer, 19M for the transformer. 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 RT-1?

RT-1 was published by Google, based in United States of America, categorised as industry.

When was RT-1 released?

RT-1 was published in December 2022. 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 RT-1 used for?

RT-1 works in Robotics, and is recorded as handling robotic manipulation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Source

Original publication

Record last updated 25 May 2026

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Looking at it from the other side?

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