Aria TPS calculator

Open weights Rhymes AI 24.9B parameters October 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

241 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Xeon Phi 7120P

16 GB · IQ4_XS · 9.6 tok/s

Fastest card

B200

136 tok/s · 180 GB

Which GPUs can run Aria?

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

241 cards match

Calculating

					Needs	Quantisation	Fit
136 tok/s 82–218 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	27.4 GB	Q8_0	Comfortable
136 tok/s 82–218 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	27.4 GB	Q8_0	Comfortable
109 tok/s 65–174 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	27.4 GB	Q8_0	Comfortable
109 tok/s 65–174 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	27.4 GB	Q8_0	Comfortable
86.9 tok/s 52–139 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	27.4 GB	Q8_0	Comfortable
83.2 tok/s 50–133 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	27.4 GB	Q8_0	Comfortable
83.2 tok/s 50–133 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	27.4 GB	Q8_0	Comfortable
79.6 tok/s 48–127 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	27.4 GB	Q8_0	Comfortable
70.7 tok/s 42–113 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	27.4 GB	Q8_0	Comfortable
70.7 tok/s 42–113 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	27.4 GB	Q8_0	Comfortable
70.7 tok/s 42–113 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	27.4 GB	Q8_0	Comfortable
67.0 tok/s 40–107 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	27.4 GB	Q8_0	Comfortable
57.2 tok/s 34–91 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	27.4 GB	Q8_0	Comfortable
57.2 tok/s 34–91 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	27.4 GB	Q8_0	Comfortable
57.2 tok/s 34–91 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	27.4 GB	Q8_0	Comfortable
57.2 tok/s 34–91 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	27.4 GB	Q8_0	Comfortable
57.2 tok/s 34–91 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	27.4 GB	Q8_0	Comfortable
47.2 tok/s 28–76 · low confidence	Tesla V100 SXM2 16 GB NVIDIA	16 GB	1,130 GB/s	Nov 2019	14.3 GB	IQ4_XS	Tight
43.5 tok/s 26–70 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	27.4 GB	Q8_0	Comfortable
43.5 tok/s 26–70 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	27.4 GB	Q8_0	Comfortable
40.1 tok/s 24–64 · low confidence	GeForce RTX 5080 NVIDIA	16 GB	960 GB/s	Jan 2025	14.3 GB	IQ4_XS	Tight
37.5 tok/s 22–60 · low confidence	Tesla V100 DGXS 16 GB NVIDIA	16 GB	897 GB/s	Mar 2018	14.3 GB	IQ4_XS	Tight
37.5 tok/s 22–60 · low confidence	Tesla V100 PCIe 16 GB NVIDIA	16 GB	897 GB/s	Jun 2017	14.3 GB	IQ4_XS	Tight
37.5 tok/s 22–60 · low confidence	Tesla V100 SXM2 16 GB NVIDIA	16 GB	897 GB/s	Jun 2017	14.3 GB	IQ4_XS	Tight
37.4 tok/s 22–60 · low confidence	GeForce RTX 5070 Ti NVIDIA	16 GB	896 GB/s	Feb 2025	14.3 GB	IQ4_XS	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: Rhymes AI
Organisation type: Industry
Country: United States of America
Published: 8 October 2024
Authors: Dongxu Li, Yudong Liu, Haoning Wu, Yue Wang, Zhiqi Shen, Bowen Qu, Xinyao Niu, Fan Zhou, Chengen Huang, Yanpeng Li, Chongyan Zhu, Xiaoyi Ren, Chao Li, Yifan Ye, Peng Liu, Lihuan Zhang, Hanshu Yan, Guoyin Wang, Bei Chen, Junnan Li

What it does

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

Domain: Multimodal, Language, Video, Vision
Task: Language modeling/generation, Visual question answering, Image captioning, Question answering, Code generation, Video description, Character recognition (OCR)

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: 24.9B
Training data: 6,800,000,000,000 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: 1.4 × 10²³ FLOP
How it was established: Operation counting

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
Hugging Face: rhymes-ai

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: 130

Sources

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

Reference: ARIA : An Open Multimodal Native Mixture-of-Experts Model
Last updated: 25 May 2026

The extremes

The ten fastest GPUs that run Aria

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 Aria

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

Xeon Phi 7120P

Memory needed

14.3 GB

Fastest

136 tok/s

With 24.9B parameters, Aria lands in the range a serious desktop card can handle once the weights are compressed. 241 of the cards we track can run it.

The entry point is the Xeon Phi 7120P: 16 GB of memory, IQ4_XS compression, roughly 9.6 tokens per second.

The quickest result comes from a B200 at around 136 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

Where it came from

Aria was published by Rhymes AI, in United States of America, in October 2024. The organisation is categorised as industry.

It works in Multimodal, Language, Video, Vision, and is recorded as doing language modeling/generation, Visual question answering, Image captioning, Question answering, Code generation, Video description, Character recognition (OCR).

The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached. It is published under the rhymes-ai organisation on Hugging Face.

Understanding the speeds

Half the cards that hold it manage more than 18.7 tokens per second, and 191 exceed reading speed outright.

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.

Without the attention layout on record, the memory column is an approximation. It is close enough to choose hardware by, and least reliable at long context.

What went into building it

Producing it required around 1.4 × 10²³ FLOP of arithmetic, which is a statement about the training budget rather than about inference.

It was trained on about 6,800,000,000,000 tokens of text.

Step by step

How to choose a GPU for Aria

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

Look at what Aria actually needs — around 14.3 GB at IQ4_XS. No amount of processing power compensates for a card that cannot hold it.
02

Decide how long your conversations run

The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context Aria can slip off a card that handles short questions easily.
03

Choose how far you will compress it

The quantisation column varies by card, because a bigger card holds a more accurate copy of Aria — 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

The speed ordering for Aria is effectively an ordering by memory bandwidth, which is why the B200 tops it at 136 tok/s.
05

Check the fit verdict before buying

The fit column separates cards that just manage Aria from those with room to spare. Buy for the second if the context might grow.
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 Aria alone — a card is usually bought for more than one model.

Answers

Aria — common questions

What GPU do I need to run Aria?

The smallest card in our catalogue that holds Aria is the Xeon Phi 7120P, with 16 GB of memory. It runs the model at IQ4_XS using about 14.3 GB, and produces roughly 9.6 tokens per second. 241 cards in total can run it.

How fast is Aria on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 136 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 191 of the cards that can run Aria clear that.

How much VRAM does Aria need?

About 14.3 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.

Can I run Aria on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at IQ4_XS, using about 14.3 GB and generating roughly 47.2 tokens per second — a tight fit.

Can I run Aria on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q6_K, using about 21.6 GB and generating roughly 33.1 tokens per second — a tight fit.

Is Aria open source?

Its weights are published, so Aria 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 Aria have?

Aria has 24.9B parameters. ARIA MoE activates 3.5B parameters per text token and has a total of 24.9B parameter. 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 Aria?

Aria was published by Rhymes AI, based in United States of America, categorised as industry.

When was Aria released?

Aria was published in October 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 Aria used for?

Aria works in Multimodal, Language, Video, Vision, and is recorded as handling language modeling/generation, Visual question answering, Image captioning, Question answering, Code generation, Video description, Character recognition (OCR). Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Where can I download Aria?

Its weights are published under the rhymes-ai 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 Aria?

Around 1.4 × 10²³ 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 Aria if it does not fit in my GPU?

Only by offloading, which is usually a false economy: the part in system memory drags the whole thing down — the nearest miss we calculate is short by 5.0 GB. Our figures for Aria assume it is fully resident.

Would two GPUs run Aria faster?

A second card roughly doubles the memory available but not the generation rate. With 241 cards already able to run Aria alone, the case for pairing is weak.

Why does the quantisation differ between cards for Aria?

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

How accurate are these Aria speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 82–218 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.

Source

Original publication

Record last updated 25 May 2026

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