T0-XXL TPS calculator

Open weights Hugging Face,Brown University 11B parameters October 2021

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 · 19.7 tok/s

Fastest card

B200

308 tok/s · 180 GB

Which GPUs can run T0-XXL?

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

509 cards match

Calculating

					Needs	Quantisation	Fit
308 tok/s 185–493 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	12.5 GB	Q8_0	Comfortable
308 tok/s 185–493 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	12.5 GB	Q8_0	Comfortable
246 tok/s 148–394 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	12.5 GB	Q8_0	Comfortable
246 tok/s 148–394 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	12.5 GB	Q8_0	Comfortable
197 tok/s 118–315 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	12.5 GB	Q8_0	Comfortable
188 tok/s 113–301 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	12.5 GB	Q8_0	Comfortable
188 tok/s 113–301 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	12.5 GB	Q8_0	Comfortable
180 tok/s 108–288 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	12.5 GB	Q8_0	Comfortable
160 tok/s 96–256 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	12.5 GB	Q8_0	Comfortable
160 tok/s 96–256 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	12.5 GB	Q8_0	Comfortable
160 tok/s 96–256 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	12.5 GB	Q8_0	Comfortable
152 tok/s 91–243 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	12.5 GB	Q8_0	Comfortable
141 tok/s 85–225 · low confidence	CMP 170HX 8 GB NVIDIA	8 GB	1,490 GB/s	Sep 2021	6.7 GB	IQ4_XS	Tight
129 tok/s 78–207 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	12.5 GB	Q8_0	Comfortable
129 tok/s 78–207 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	12.5 GB	Q8_0	Comfortable
129 tok/s 78–207 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	12.5 GB	Q8_0	Comfortable
129 tok/s 78–207 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	12.5 GB	Q8_0	Comfortable
129 tok/s 78–207 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	12.5 GB	Q8_0	Comfortable
107 tok/s 64–172 · low confidence	CMP 170HX 10 GB NVIDIA	10 GB	1,560 GB/s	Sep 2021	8.6 GB	Q5_K_M	Tight
98.5 tok/s 59–158 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	12.5 GB	Q8_0	Comfortable
98.5 tok/s 59–158 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	12.5 GB	Q8_0	Comfortable
82.1 tok/s 49–131 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	12.5 GB	Q8_0	Comfortable
80.3 tok/s 48–129 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	12.5 GB	Q8_0	Comfortable
78.6 tok/s 47–126 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	12.5 GB	Q8_0	Comfortable
78.6 tok/s 47–126 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	12.5 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: Hugging Face,Brown University
Organisation type: Industry,Academia
Country: United States of America
Published: 15 October 2021
Authors: Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal V. Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng-Xin Yong, Harshit…

What it does

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

Domain: Language
Task: Language modeling
Base model: T5-11B

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: 11B
Training data: 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: 9.2 × 10²⁰ FLOP
How it was established: Hardware

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
Chips used: 256
Chip-hours: 69,120
Wall-clock time: 27 hours
Power draw: 232.4 kW
Compute cost: $11,672

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.

Record confidence: Confident
Citations: 1,988

Sources

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

Reference: Multitask Prompted Training Enables Zero-Shot Task Generalization
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run T0-XXL

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 T0-XXL

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

6.7 GB

Fastest

308 tok/s

T0-XXL reaches a parameter count of 11B. That is small enough that hardware is rarely the obstacle, including on cards several years old. The number of cards we track that can run it: 509.

The least hardware that works is Xeon Phi 5110P, with a memory capacity of 8 GB, running it at a compression of IQ4_XS and producing around 19.7 tokens per second.

Top of the range is B200, generating roughly 308 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.

Background

T0-XXL was published by Hugging Face,Brown University, in the country recorded as United States of America, during October 2021. It comes out of an organisation categorised as industry,Academia.

It works in the domain of Language, and is recorded as performing the task of language modeling.

Rather than being trained from scratch, it is derived from T5-11B. That is the usual way a specialised model is produced.

Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded.

Reading the throughput figures

The median result is around 21.2 tokens per second. Exceeding reading speed outright: 460 of them.

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 internal architecture is not on file, so memory is approximated from the parameter count and marked accordingly. Expect the real figure to differ, more so at long context.

Training and provenance

Training it took a computation budget of roughly 9.2 × 10²⁰ FLOP, on hardware recorded as Google TPU v3. That figure measures what producing the model cost, and has no bearing on how fast it answers.

Step by step

How to choose a GPU for T0-XXL

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

Every card here has been checked against T0-XXL, needing around 6.7 GB at a compression of IQ4_XS. Capacity is the gate — a card either holds it or it does not.
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, because at long context a card that handles short questions easily can be dropped by T0-XXL.
03

Set a quality floor

Compression is what makes a model fit smaller cards, at some cost in accuracy, reaching a compression of IQ4_XS on the smallest card that fits. Setting a minimum quality drops the cards that only manage it by squeezing further than you would want, and holds the comparison at one level.
04

Sort by speed

The speed ordering is effectively an ordering by memory bandwidth, for T0-XXL. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B200, at 308 tok/s.
05

Read the fit column last

The fit column separates cards that just manage it from those with room to spare, in the case of T0-XXL. Comfortable means you can grow the context later. That difference matters more than a few tokens per second, so buy for comfortable if you expect to.
06

Check the card from the other side

Each card page repeats this sweep for every model we hold, answering what else the hardware is good for beyond T0-XXL.

Answers

T0-XXL — common questions

T0-XXL— how much compute was used to train it?

Training consumed around 9.2 × 10²⁰ FLOP, on hardware recorded as 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.

T0-XXL— can I run it if it does not fit in my GPU?

It can be split between the card and system memory, but it generates painfully slowly that way. The nearest miss we calculate falls short by 2.0 GB. Every figure here assumes the whole model is resident on the card.

T0-XXL— would two GPUs run it faster?

A second card roughly doubles the memory available but not the generation rate. The number already able to run it alone: 509. So a second card is rarely the answer here.

T0-XXL— why does the quantisation differ between cards?

Each card is shown running the least-compressed copy it can hold. The number of distinct compression levels across the cards that fit it: 4. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

T0-XXL— how accurate are these speed estimates?

These are estimates with real error bars, and any of them could reasonably land anywhere in its published range depending on which runtime you use. The fastest result here: 185–493 tok/s on B200. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

T0-XXL— what GPU do I need to run it?

The smallest card in our catalogue that holds it is Xeon Phi 5110P, with a memory capacity of 8 GB. It runs the model at a compression of IQ4_XS using about 6.7 GB, and produces roughly 19.7 tokens per second. The number of cards able to run it in total: 509.

T0-XXL— how fast is it on a GPU?

It depends on the card. The quickest we calculate is B200, at about 308 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and the number of cards clearing that: 460.

T0-XXL— how much VRAM does it need?

It needs about 6.7 GB at a compression of IQ4_XS, 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.

T0-XXL— can I run it on a GPU holding 8 GB?

Yes. The card CMP 170HX 8 GB, holding 8 GB, runs it at a compression of IQ4_XS, using about 6.7 GB and generating roughly 141 tokens per second. The fit is tight.

T0-XXL— can I run it on a GPU holding 12 GB?

Yes. The card GeForce RTX 3080 Ti, holding 12 GB, runs it at a compression of Q6_K, using about 9.9 GB and generating roughly 51.0 tokens per second. The fit is tight.

T0-XXL— can I run it on a GPU holding 16 GB?

Yes. The card Tesla V100 SXM2 16 GB, holding 16 GB, runs it at a compression of Q8_0, using about 12.5 GB and generating roughly 43.5 tokens per second. The fit is tight.

T0-XXL— can I run it on a GPU holding 24 GB?

Yes. The card GeForce RTX 5090 D V2, holding 24 GB, runs it at a compression of Q8_0, using about 12.5 GB and generating roughly 51.6 tokens per second. The fit is comfortable.

T0-XXL— is it open source?

Its weights are published, so it 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.

T0-XXL— how many parameters does it have?

It has a parameter count of 11B. "Unless specified otherwise, we use the XXL version which has 11B 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.

T0-XXL— who created it?

It was published by Hugging Face,Brown University, based in United States of America, an organisation categorised as industry,Academia.

T0-XXL— when was it released?

It was published in October 2021. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.

T0-XXL— what is it used for?

It works in the domain of Language, and is recorded as handling the task of language modeling. 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.

T0-XXL— where can I download it?

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

Source

Original publication

Record last updated 25 May 2026

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