MAGNeT TPS calculator

Open weights Meta AI,Hebrew University of Jerusalem,Kyutai 1.5B parameters January 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

818 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Tesla C1080

4 GB · Q8_0 · 24.6 tok/s

Fastest card

B200

2,259 tok/s · 180 GB

Which GPUs can run MAGNeT?

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

818 cards match

Calculating

					Needs	Quantisation	Fit
2,259 tok/s 1,355–3,614 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	2.3 GB	Q8_0	Comfortable
2,259 tok/s 1,355–3,614 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	2.3 GB	Q8_0	Comfortable
1,804 tok/s 1,082–2,886 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	2.3 GB	Q8_0	Comfortable
1,804 tok/s 1,082–2,886 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	2.3 GB	Q8_0	Comfortable
1,443 tok/s 866–2,308 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	2.3 GB	Q8_0	Comfortable
1,381 tok/s 828–2,209 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	2.3 GB	Q8_0	Comfortable
1,381 tok/s 828–2,209 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	2.3 GB	Q8_0	Comfortable
1,321 tok/s 793–2,114 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	2.3 GB	Q8_0	Comfortable
1,173 tok/s 704–1,876 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	2.3 GB	Q8_0	Comfortable
1,173 tok/s 704–1,876 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	2.3 GB	Q8_0	Comfortable
1,173 tok/s 704–1,876 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	2.3 GB	Q8_0	Comfortable
1,112 tok/s 667–1,780 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	2.3 GB	Q8_0	Comfortable
949 tok/s 569–1,518 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	2.3 GB	Q8_0	Comfortable
949 tok/s 569–1,518 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	2.3 GB	Q8_0	Comfortable
949 tok/s 569–1,518 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	2.3 GB	Q8_0	Comfortable
949 tok/s 569–1,518 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	2.3 GB	Q8_0	Comfortable
949 tok/s 569–1,518 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	2.3 GB	Q8_0	Comfortable
722 tok/s 433–1,156 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	2.3 GB	Q8_0	Comfortable
722 tok/s 433–1,156 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	2.3 GB	Q8_0	Comfortable
602 tok/s 361–963 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	2.3 GB	Q8_0	Comfortable
589 tok/s 353–943 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	2.3 GB	Q8_0	Comfortable
576 tok/s 346–922 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	2.3 GB	Q8_0	Comfortable
576 tok/s 346–922 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	2.3 GB	Q8_0	Comfortable
576 tok/s 346–922 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	2.3 GB	Q8_0	Comfortable
576 tok/s 346–922 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	2.3 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: Meta AI,Hebrew University of Jerusalem,Kyutai
Organisation type: Industry,Academia,Industry
Country: United States of America, Israel, France
Published: 9 January 2024
Authors: Alon Ziv, Itai Gat, Gael Le Lan, Tal Remez, Felix Kreuk, Alexandre Défossez, Jade Copet, Gabriel Synnaeve, Yossi Adi

What it does

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

Domain: Audio
Task: Audio generation

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: 1.5B
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)

How it is classified

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

Record confidence: Likely

Sources

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

Reference: Masked Audio Generation using a Single Non-Autoregressive Transformer
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run MAGNeT

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 MAGNeT

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

Tesla C1080

Memory needed

2.3 GB

Fastest

2,259 tok/s

MAGNeT is small enough at 1.5B parameters that hardware is rarely the obstacle — 818 of the cards we track can run it, including cards several years old.

The least hardware that works is a Tesla C1080. Its 4 GB is enough at Q8_0 compression, giving roughly 24.6 tokens per second.

At the other end, a B200 generates roughly 2,259 tokens per second on it, on the strength of 8,000 GB/s of memory bandwidth.

About this model

MAGNeT was published by Meta AI,Hebrew University of Jerusalem,Kyutai, in United States of America, in January 2024. The organisation is categorised as industry,Academia,Industry.

It works in Audio, and is recorded as doing audio generation.

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

How fast it runs, and why

Half the cards that hold it manage more than 63.4 tokens per second, and 796 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 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.

Step by step

How to choose a GPU for MAGNeT

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

Every card here has been checked against MAGNeT — around 2.3 GB at Q8_0. Capacity is the gate — a card either holds it or it does not.
02

Set the context length you will work at

Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason MAGNeT stops fitting a card that seemed fine.
03

Choose how far you will compress it

Each card runs the least-compressed copy it can hold — Q8_0 on the smallest card that fits. Setting a floor drops the cards that only manage MAGNeT by squeezing it further than you would want.
04

Rank by throughput rather than spec sheet

Ranking by tokens per second for MAGNeT follows memory bandwidth, not core counts, which is why the B200 tops it at 2,259 tok/s.
05

Look at the headroom, not just the fit

The fit column separates cards that just manage MAGNeT 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 MAGNeT alone — a card is usually bought for more than one model.

Answers

MAGNeT — common questions

Who created MAGNeT?

MAGNeT was published by Meta AI,Hebrew University of Jerusalem,Kyutai, based in United States of America, categorised as industry,Academia,Industry.

When was MAGNeT released?

MAGNeT was published in January 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 MAGNeT used for?

MAGNeT works in Audio, and is recorded as handling audio generation. 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 MAGNeT?

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

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

It can be split between the card and system memory, but MAGNeT generates painfully slowly that way. Nothing on this page assumes offloading.

Would two GPUs run MAGNeT faster?

Capacity adds across cards; throughput does not. Since 818 of the cards we track already hold MAGNeT on their own, a second card is rarely the answer here.

Why does the quantisation differ between cards for MAGNeT?

A larger card holds a more accurate copy. Across the cards that run MAGNeT, 1 compression levels are used; the floor control above pins it to one.

How accurate are these MAGNeT speed estimates?

These are estimates with real error bars. The fastest result here, 1,355–3,614 tok/s on the B200, could reasonably land anywhere in its published range depending on which runtime you use.

What GPU do I need to run MAGNeT?

The smallest card in our catalogue that holds MAGNeT is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 2.3 GB, and produces roughly 24.6 tokens per second. 818 cards in total can run it.

How fast is MAGNeT on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 2,259 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 796 of the cards that can run MAGNeT clear that.

How much VRAM does MAGNeT need?

About 2.3 GB at Q8_0 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 MAGNeT on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 2.3 GB and generating roughly 421 tokens per second — a comfortable fit.

Can I run MAGNeT on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 2.3 GB and generating roughly 258 tokens per second — a comfortable fit.

Can I run MAGNeT on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 2.3 GB and generating roughly 319 tokens per second — a comfortable fit.

Can I run MAGNeT on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 2.3 GB and generating roughly 378 tokens per second — a comfortable fit.

Is MAGNeT open source?

Its weights are published, so MAGNeT 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 MAGNeT have?

MAGNeT has 1.5B parameters. "We train non-autoregressive transformer models using 300M (MAGNET-small) and 1.5B (MAGNET-large) 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

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