OpenFlamingo TPS calculator

Open weights University of Washington,Stanford University,Allen Institute for AI,Hebrew University of Jerusalem,Columbia University,Google DeepMind,University of California Santa Barbara (UCSB),Research Center Juelich 9B parameters August 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

582 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Quadro 6000

6 GB · Q3_K_M · 15.5 tok/s

Fastest card

B200

376 tok/s · 180 GB

Which GPUs can run OpenFlamingo?

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

582 cards match

Calculating

					Needs	Quantisation	Fit
376 tok/s 226–602 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	10.3 GB	Q8_0	Comfortable
376 tok/s 226–602 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	10.3 GB	Q8_0	Comfortable
301 tok/s 180–481 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	10.3 GB	Q8_0	Comfortable
301 tok/s 180–481 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	10.3 GB	Q8_0	Comfortable
240 tok/s 144–385 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	10.3 GB	Q8_0	Comfortable
230 tok/s 138–368 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	10.3 GB	Q8_0	Comfortable
230 tok/s 138–368 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	10.3 GB	Q8_0	Comfortable
220 tok/s 132–352 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	10.3 GB	Q8_0	Comfortable
195 tok/s 117–313 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	10.3 GB	Q8_0	Comfortable
195 tok/s 117–313 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	10.3 GB	Q8_0	Comfortable
195 tok/s 117–313 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	10.3 GB	Q8_0	Comfortable
185 tok/s 111–297 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	10.3 GB	Q8_0	Comfortable
158 tok/s 95–253 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	10.3 GB	Q8_0	Comfortable
158 tok/s 95–253 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	10.3 GB	Q8_0	Comfortable
158 tok/s 95–253 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	10.3 GB	Q8_0	Comfortable
158 tok/s 95–253 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	10.3 GB	Q8_0	Comfortable
158 tok/s 95–253 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	10.3 GB	Q8_0	Comfortable
125 tok/s 75–200 · low confidence	CMP 170HX 8 GB NVIDIA	8 GB	1,490 GB/s	Sep 2021	7.2 GB	Q5_K_M	Tight
120 tok/s 72–193 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	10.3 GB	Q8_0	Comfortable
120 tok/s 72–193 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	10.3 GB	Q8_0	Comfortable
107 tok/s 64–171 · low confidence	CMP 170HX 10 GB NVIDIA	10 GB	1,560 GB/s	Sep 2021	8.2 GB	Q6_K	Tight
100 tok/s 60–161 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	10.3 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	10.3 GB	Q8_0	Comfortable
96.0 tok/s 58–154 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	10.3 GB	Q8_0	Comfortable
96.0 tok/s 58–154 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	10.3 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: University of Washington,Stanford University,Allen Institute for AI,Hebrew University of Jerusalem,Columbia University,Google DeepMind,University of California Santa Barbara (UCSB),Research Center Juelich
Organisation type: Academia,Academia,Research collective,Academia,Academia,Industry,Academia
Country: United States of America, Israel, Germany
Published: 2 August 2023
Authors: Anas Awadalla, Irena Gao, Josh Gardner, Jack Hessel, Yusuf Hanafy, Wanrong Zhu, Kalyani Marathe, Yonatan Bitton, Samir Gadre, Shiori Sagawa, Jenia Jitsev, Simon Kornblith, Pang Wei Koh, Gabriel Ilharco, Mitchell Wortsman, Ludwig Schmidt

What it does

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

Domain: Image generation
Task: Image generation, Text-to-image

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: 17,400,000,000 tokens

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: NVIDIA A100 SXM4 80 GB
Chips used: 64
Power draw: 50.9 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 (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

Sources

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

Reference: OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run OpenFlamingo

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 OpenFlamingo

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

Quadro 6000

Memory needed

5.1 GB

Fastest

376 tok/s

OpenFlamingo reaches a parameter count of 9B. 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: 582.

The least hardware that works is Quadro 6000, with a memory capacity of 6 GB, running it at a compression of Q3_K_M and producing around 15.5 tokens per second.

The fastest we calculate for it is B200, generating roughly 376 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.

Where it came from

OpenFlamingo was published by University of Washington,Stanford University,Allen Institute for AI,Hebrew University of Jerusalem,Columbia University,Google DeepMind,University of California Santa Barbara (UCSB),Research Center Juelich, in the country recorded as United States of America, during August 2023. The publishing organisation is categorised as academia,Academia,Research collective,Academia,Academia,Industry,Academia.

It works in the domain of Image generation, and is recorded as performing the task of image generation, Text-to-image.

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.

Understanding the speeds

Half the cards that hold it manage more than 21.1 tokens per second. Clearing the ten tokens per second that roughly matches reading speed: 541 of them.

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

Training consumed a corpus of around 17,400,000,000 tokens of text.

Step by step

How to choose a GPU for OpenFlamingo

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

Start from what it actually needs, which is the requirement of OpenFlamingo, needing around 5.1 GB at a compression of Q3_K_M. Capacity is the gate — a card either holds it or it does not.
02

Match the context to your actual use

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

Decide how much compression you will accept

Compression is what makes a model fit smaller cards, at some cost in accuracy, reaching a compression of Q3_K_M 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

Rank by throughput rather than spec sheet

The speed ordering is effectively an ordering by memory bandwidth, for OpenFlamingo. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B200, at 376 tok/s.
05

Check the fit verdict before buying

Tight means it loads and works with no room to raise the context later, in the case of OpenFlamingo. 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

Every card name links to its own page, which runs the same calculation across the whole model catalogue. A card is usually bought for more than one model, so it is worth a look before buying for OpenFlamingo.

Answers

OpenFlamingo — common questions

OpenFlamingo— who created it?

It was published by University of Washington,Stanford University,Allen Institute for AI,Hebrew University of Jerusalem,Columbia University,Google DeepMind,University of California Santa Barbara (UCSB),Research Center Juelich, based in United States of America, an organisation categorised as academia,Academia,Research collective,Academia,Academia,Industry,Academia.

OpenFlamingo— when was it released?

It was published in August 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.

OpenFlamingo— what is it used for?

It works in the domain of Image generation, and is recorded as handling the task of image generation, Text-to-image. 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.

OpenFlamingo— 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.

OpenFlamingo— can I run it if it does not fit in my GPU?

Only by offloading, which is usually a false economy: the part held in system memory drags the whole thing down. The nearest miss we calculate falls short by 1.6 GB. Every figure here assumes the whole model is resident on the card.

OpenFlamingo— would two GPUs run it faster?

Capacity adds across cards; throughput does not. The number of cards already holding it on their own: 582. So a second card is rarely the answer here.

OpenFlamingo— why does the quantisation differ between cards?

Because capacity varies, so does how hard it has to be squeezed. The number of distinct levels in the table above: 4. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

OpenFlamingo— 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: 226–602 tok/s on B200. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

OpenFlamingo— what GPU do I need to run it?

The smallest card in our catalogue that holds it is Quadro 6000, with a memory capacity of 6 GB. It runs the model at a compression of Q3_K_M using about 5.1 GB, and produces roughly 15.5 tokens per second. The number of cards able to run it in total: 582.

OpenFlamingo— how fast is it on a GPU?

It depends on the card. The quickest we calculate is 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 the number of cards clearing that: 541.

OpenFlamingo— how much VRAM does it need?

It needs about 5.1 GB at a compression of Q3_K_M, 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.

OpenFlamingo— 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 Q5_K_M, using about 7.2 GB and generating roughly 125 tokens per second. The fit is tight.

OpenFlamingo— 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 Q8_0, using about 10.3 GB and generating roughly 42.9 tokens per second. The fit is tight.

OpenFlamingo— 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 10.3 GB and generating roughly 53.2 tokens per second. The fit is comfortable.

OpenFlamingo— 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 10.3 GB and generating roughly 63.1 tokens per second. The fit is comfortable.

OpenFlamingo— 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.

OpenFlamingo— how many parameters does it have?

It has a parameter count of 9B. "We introduce OpenFlamingo, a family of autoregressive vision-language models ranging from 3B to 9B 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.

Source

Original publication

Record last updated 28 November 2025

The other direction

Looking at it from the other side?

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