Flan UL2 TPS calculator

Open weights Google Brain 19.5B parameters March 2023

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

293 of 818 cards that can run it

Smallest card that fits

Quadro K6000

12 GB · Q3_K_M · 14.4 tok/s

Fastest card

B200

174 tok/s · 180 GB

Which GPUs can run Flan UL2?

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

293 cards match

Calculating

					Needs	Quantisation	Fit
174 tok/s 104–278 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	21.6 GB	Q8_0	Comfortable
174 tok/s 104–278 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	21.6 GB	Q8_0	Comfortable
139 tok/s 83–222 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	21.6 GB	Q8_0	Comfortable
139 tok/s 83–222 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	21.6 GB	Q8_0	Comfortable
111 tok/s 67–178 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	21.6 GB	Q8_0	Comfortable
106 tok/s 64–170 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	21.6 GB	Q8_0	Comfortable
106 tok/s 64–170 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	21.6 GB	Q8_0	Comfortable
102 tok/s 61–163 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	21.6 GB	Q8_0	Comfortable
90.2 tok/s 54–144 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	21.6 GB	Q8_0	Comfortable
90.2 tok/s 54–144 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	21.6 GB	Q8_0	Comfortable
90.2 tok/s 54–144 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	21.6 GB	Q8_0	Comfortable
85.6 tok/s 51–137 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	21.6 GB	Q8_0	Comfortable
73.0 tok/s 44–117 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	21.6 GB	Q8_0	Comfortable
73.0 tok/s 44–117 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	21.6 GB	Q8_0	Comfortable
73.0 tok/s 44–117 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	21.6 GB	Q8_0	Comfortable
73.0 tok/s 44–117 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	21.6 GB	Q8_0	Comfortable
73.0 tok/s 44–117 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	21.6 GB	Q8_0	Comfortable
56.7 tok/s 34–91 · low confidence	Tesla V100 SXM2 16 GB NVIDIA	16 GB	1,130 GB/s	Nov 2019	12.5 GB	Q4_K_M	Tight
55.6 tok/s 33–89 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	21.6 GB	Q8_0	Comfortable
55.6 tok/s 33–89 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	21.6 GB	Q8_0	Comfortable
53.5 tok/s 32–86 · low confidence	GeForce RTX 3080 12 GB NVIDIA	12 GB	912 GB/s	Jan 2022	10.2 GB	Q3_K_M	Tight
53.5 tok/s 32–86 · low confidence	GeForce RTX 3080 Ti NVIDIA	12 GB	912 GB/s	May 2021	10.2 GB	Q3_K_M	Tight
48.1 tok/s 29–77 · low confidence	GeForce RTX 5080 NVIDIA	16 GB	960 GB/s	Jan 2025	12.5 GB	Q4_K_M	Tight
46.3 tok/s 28–74 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	21.6 GB	Q8_0	Comfortable
45.3 tok/s 27–73 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	21.6 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 Brain
Organisation type: Industry
Country: United States of America
Published: 3 March 2023
Authors: Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Jason Wei, Xuezhi Wang, Hyung Won Chung, Siamak Shakeri, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Denny Zhou, Neil Houlsby, Donald Metzler

What it does

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

Domain: Language
Task: Language generation
Base model: UL2

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: 19.5B
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: Unreleased

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: 387

Sources

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

Reference: A New Open Source Flan 20B with UL2
Last updated: 25 May 2026

The extremes

The ten fastest GPUs for Flan UL2

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 Flan UL2

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

The hardware side

Minimum card

Quadro K6000

Memory needed

10.2 GB

Fastest

174 tok/s

Flan UL2 is small enough at 19.5B parameters that hardware is rarely the obstacle — 293 of the cards we track can run it, including cards several years old.

At the low end, a Quadro K6000 handles it — 12 GB, at Q3_K_M, for about 14.4 tokens per second.

At the other end, a B200 generates roughly 174 tokens per second on it, on the strength of 8,000 GB/s of memory bandwidth.

What this model is

Flan UL2 was published by Google Brain, in United States of America, in March 2023. The organisation is categorised as industry.

It works in Language, and is recorded as doing language generation.

It builds on UL2, which is why it shares that model's general shape and size.

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

What decides the speed

Half the cards that hold it manage more than 19.8 tokens per second, and 249 exceed reading speed outright.

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.

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.

Step by step

How to choose a GPU for Flan UL2

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 Flan UL2 — around 10.2 GB at Q3_K_M. Capacity is the gate — a card either holds it or it does not.
02

Set the context length you will work at

Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for Flan UL2.
03

Decide how much compression you will accept

Compression is what makes Flan UL2 fit smaller cards, at some cost in accuracy — Q3_K_M 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 Flan UL2 is effectively an ordering by memory bandwidth, which is why the B200 tops it at 174 tok/s.
05

Check the fit verdict before buying

Tight means Flan UL2 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

Check the card from the other side

Following a card through to its own page shows every other model it can hold, which is the question that follows once Flan UL2 is settled.

Answers

Flan UL2 — common questions

Can I run Flan UL2 if it does not fit in my GPU?

It can be split between the card and system memory, but Flan UL2 generates painfully slowly that way — the nearest miss we calculate is short by 2.6 GB. Nothing on this page assumes offloading.

Would two GPUs run Flan UL2 faster?

A second card roughly doubles the memory available but not the generation rate. With 293 cards already able to run Flan UL2 alone, the case for pairing is weak.

Why does the quantisation differ between cards for Flan UL2?

A larger card holds a more accurate copy. Across the cards that run Flan UL2, 4 compression levels are used; the floor control above pins it to one.

How accurate are these Flan UL2 speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 104–278 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 Flan UL2?

The smallest card in our catalogue that holds Flan UL2 is the Quadro K6000, with 12 GB of memory. It runs the model at Q3_K_M using about 10.2 GB, and produces roughly 14.4 tokens per second. 293 cards in total can run it.

How fast is Flan UL2 on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 174 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 249 of the cards that can run Flan UL2 clear that.

How much VRAM does Flan UL2 need?

About 10.2 GB at Q3_K_M 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 Flan UL2 on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q3_K_M, using about 10.2 GB and generating roughly 53.5 tokens per second — a tight fit.

Can I run Flan UL2 on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q4_K_M, using about 12.5 GB and generating roughly 56.7 tokens per second — a tight fit.

Can I run Flan UL2 on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 21.6 GB and generating roughly 29.1 tokens per second — a tight fit.

Is Flan UL2 open source?

Its weights are published, so Flan UL2 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 Flan UL2 have?

Flan UL2 has 19.5B parameters. 19.5B per https://www.yitay.net/blog/flan-ul2-20b. 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 Flan UL2?

Flan UL2 was published by Google Brain, based in United States of America, categorised as industry.

When was Flan UL2 released?

Flan UL2 was published in March 2023. 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 Flan UL2 used for?

Flan UL2 works in Language, and is recorded as handling language generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Where can I download Flan UL2?

The weights for Flan UL2 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

The other direction

Looking at it from the other side?

This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.

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