Qwen1.5-72B TPS calculator

Open weights Alibaba 72B parameters February 2024

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

43 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Radeon Instinct MI200

64 GB · IQ4_XS · 18.5 tok/s

Fastest card

B200

47.1 tok/s · 180 GB

Which GPUs can run Qwen1.5-72B?

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

43 cards match

Calculating

					Needs	Quantisation	Fit
47.1 tok/s 40–56	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	93.3 GB	Q8_0	Comfortable
47.1 tok/s 40–56	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	93.3 GB	Q8_0	Comfortable
37.6 tok/s 23–60 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	93.3 GB	Q8_0	Comfortable
37.6 tok/s 23–60 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	93.3 GB	Q8_0	Comfortable
35.3 tok/s 30–42	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	68.1 GB	Q5_K_M	Tight
35.3 tok/s 30–42	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	68.1 GB	Q5_K_M	Tight
33.7 tok/s 29–40	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	76.5 GB	Q6_K	Tight
30.1 tok/s 18–48 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	93.3 GB	Q8_0	Comfortable
29.2 tok/s 25–35	H100 SXM5 64 GB NVIDIA	64 GB	2,020 GB/s	Mar 2023	55.6 GB	IQ4_XS	Tight
28.8 tok/s 24–35	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	93.3 GB	Q8_0	Comfortable
28.8 tok/s 24–35	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	93.3 GB	Q8_0	Comfortable
28.7 tok/s 24–34	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	76.5 GB	Q6_K	Tight
28.7 tok/s 24–34	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	76.5 GB	Q6_K	Tight
28.7 tok/s 24–34	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	76.5 GB	Q6_K	Tight
27.5 tok/s 17–44 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	93.3 GB	Q8_0	Comfortable
24.4 tok/s 15–39 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	93.3 GB	Q8_0	Comfortable
24.4 tok/s 15–39 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	93.3 GB	Q8_0	Comfortable
24.4 tok/s 15–39 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	93.3 GB	Q8_0	Comfortable
21.4 tok/s 18–26	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	68.1 GB	Q5_K_M	Tight
21.4 tok/s 18–26	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	68.1 GB	Q5_K_M	Tight
21.4 tok/s 18–26	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	68.1 GB	Q5_K_M	Tight
21.4 tok/s 18–26	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	68.1 GB	Q5_K_M	Tight
21.4 tok/s 18–26	H100 PCIe 80 GB NVIDIA	80 GB	2,040 GB/s	Oct 2022	68.1 GB	Q5_K_M	Tight
21.4 tok/s 18–26	H800 PCIe 80 GB NVIDIA	80 GB	2,040 GB/s	Mar 2023	68.1 GB	Q5_K_M	Tight
20.4 tok/s 17–24	A100 PCIe 80 GB NVIDIA	80 GB	1,940 GB/s	Jun 2021	68.1 GB	Q5_K_M	Tight

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: Alibaba
Organisation type: Industry
Country: China
Published: 4 February 2024
Authors: Qwen Team

What it does

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

Domain: Language
Task: Chat, Language modeling/generation, Quantitative reasoning, Code generation, Translation

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: 72B
Training data: 3,000,000,000,000 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: 1.3 × 10²⁴ FLOP
How it was established: Operation counting

The training run

What it physically took to train: which chips, how many, for how long, and what that drew from the wall.

Data centre: There is no paper to reference, no information about hardware used for training found in media.

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 (restricted use)
Training code: Unreleased
Hugging Face: Qwen

How it is classified

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

Likely above 10²³ FLOP: Yes
Why it is tracked: SOTA improvement
Record confidence: Confident

Sources

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

Reference: Introducing Qwen1.5
Last updated: 18 December 2025

The extremes

The ten fastest GPUs that run Qwen1.5-72B

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 Qwen1.5-72B

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

Radeon Instinct MI200

Memory needed

55.6 GB

Fastest

47.1 tok/s

Qwen1.5-72B sits at 72B parameters, which puts it above consumer hardware and into the range where a card is bought for this purpose rather than repurposed for it. 43 of the cards we track can hold it.

The entry point is the Radeon Instinct MI200: 64 GB of memory, IQ4_XS compression, roughly 18.5 tokens per second.

The quickest result comes from a B200 at around 47.1 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

Background

Qwen1.5-72B was published by Alibaba, in China, in February 2024. industry is the category the publisher falls under.

It works in Language, and is recorded as doing chat, Language modeling/generation, Quantitative reasoning, Code generation, Translation.

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. It is published under the Qwen organisation on Hugging Face.

Reading the throughput figures

Half the cards that hold it manage more than 21.4 tokens per second, and 39 exceed reading speed outright.

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

Its attention layout is on file, so the memory figures are computed exactly rather than approximated.

How it was trained

Producing it required around 1.3 × 10²⁴ FLOP of arithmetic, which is a statement about the training budget rather than about inference.

It was trained on about 3,000,000,000,000 tokens of text.

Its inclusion criterion is sOTA improvement.

Step by step

How to choose a GPU for Qwen1.5-72B

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 Qwen1.5-72B — around 55.6 GB at IQ4_XS. 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 Qwen1.5-72B.
03

Set a quality floor

The quantisation column varies by card, because a bigger card holds a more accurate copy of Qwen1.5-72B — IQ4_XS on the smallest card that fits. Set a floor to hold the comparison at one level.
04

Compare tokens per second, not specifications

Ranking by tokens per second for Qwen1.5-72B follows memory bandwidth, not core counts, which is why the B200 tops it at 47.1 tok/s.
05

Look at the headroom, not just the fit

A tight fit runs Qwen1.5-72B but leaves nothing spare for a longer conversation; comfortable has headroom. If you expect to grow the context, buy for comfortable.
06

Open the card you have settled on

Every card name links to its own page, which runs the same calculation across the whole model catalogue. Worth a look before buying for Qwen1.5-72B alone — a card is usually bought for more than one model.

Answers

Qwen1.5-72B — common questions

How much VRAM does Qwen1.5-72B need?

About 55.6 GB at IQ4_XS 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.

Is Qwen1.5-72B open source?

Its weights are published, so Qwen1.5-72B 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 Qwen1.5-72B have?

Qwen1.5-72B has 72B parameters. 72B. 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 Qwen1.5-72B?

Qwen1.5-72B was published by Alibaba, based in China, categorised as industry.

When was Qwen1.5-72B released?

Qwen1.5-72B was published in February 2024. 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 Qwen1.5-72B used for?

Qwen1.5-72B works in Language, and is recorded as handling chat, Language modeling/generation, Quantitative reasoning, Code generation, Translation. 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 Qwen1.5-72B?

Its weights are published under the Qwen organisation on Hugging Face. We do not host model files — this site calculates what hardware is needed to run them.

How much compute was used to train Qwen1.5-72B?

Around 1.3 × 10²⁴ FLOP. 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 Qwen1.5-72B if it does not fit in my GPU?

It can be split between the card and system memory, but Qwen1.5-72B generates painfully slowly that way — the nearest miss we calculate is short by 16.6 GB. Nothing on this page assumes offloading.

Would two GPUs run Qwen1.5-72B faster?

Two cards buy memory rather than speed. That matters for Qwen1.5-72B only if one card cannot hold it — 43 can, so a second adds little.

Why does the quantisation differ between cards for Qwen1.5-72B?

A larger card holds a more accurate copy. Across the cards that run Qwen1.5-72B, 5 compression levels are used; the floor control above pins it to one.

How accurate are these Qwen1.5-72B speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 40–56 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 Qwen1.5-72B?

The smallest card in our catalogue that holds Qwen1.5-72B is the Radeon Instinct MI200, with 64 GB of memory. It runs the model at IQ4_XS using about 55.6 GB, and produces roughly 18.5 tokens per second. 43 cards in total can run it.

How fast is Qwen1.5-72B on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 47.1 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 39 of the cards that can run Qwen1.5-72B clear that.

Source

Original publication

Record last updated 18 December 2025

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