Nemotron 3-Nano-30B-A3B TPS calculator

Open weights NVIDIA 31.6B parameters December 2025

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 of 818 cards that can run it

Smallest card that fits

Xeon Phi 7120P

16 GB · Q3_K_M · 46.0 tok/s

Fastest card

B200

596 tok/s · 180 GB

Which GPUs can run Nemotron 3-Nano-30B-A3B?

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
596 tok/s 357–953 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	32.8 GB	Q8_0	Comfortable
596 tok/s 357–953 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	32.8 GB	Q8_0	Comfortable
476 tok/s 285–761 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	32.8 GB	Q8_0	Comfortable
476 tok/s 285–761 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	32.8 GB	Q8_0	Comfortable
380 tok/s 228–609 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	32.8 GB	Q8_0	Comfortable
364 tok/s 218–583 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	32.8 GB	Q8_0	Comfortable
364 tok/s 218–583 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	32.8 GB	Q8_0	Comfortable
348 tok/s 209–558 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	32.8 GB	Q8_0	Comfortable
309 tok/s 186–495 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	32.8 GB	Q8_0	Comfortable
309 tok/s 186–495 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	32.8 GB	Q8_0	Comfortable
309 tok/s 186–495 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	32.8 GB	Q8_0	Comfortable
293 tok/s 176–469 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	32.8 GB	Q8_0	Comfortable
250 tok/s 150–400 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	32.8 GB	Q8_0	Comfortable
250 tok/s 150–400 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	32.8 GB	Q8_0	Comfortable
250 tok/s 150–400 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	32.8 GB	Q8_0	Comfortable
250 tok/s 150–400 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	32.8 GB	Q8_0	Comfortable
250 tok/s 150–400 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	32.8 GB	Q8_0	Comfortable
230 tok/s 138–369 · low confidence	GeForce RTX 5090 D V2 NVIDIA	24 GB	1,340 GB/s	Aug 2025	18.1 GB	Q4_K_M	Tight
227 tok/s 136–363 · low confidence	Tesla V100 SXM2 16 GB NVIDIA	16 GB	1,130 GB/s	Nov 2019	14.4 GB	Q3_K_M	Tight
210 tok/s 126–336 · low confidence	A30X NVIDIA	24 GB	1,220 GB/s	Apr 2021	18.1 GB	Q4_K_M	Tight
202 tok/s 121–324 · low confidence	DRIVE A100 PROD NVIDIA	32 GB	1,870 GB/s	May 2020	25.4 GB	Q6_K	Tight
202 tok/s 121–324 · low confidence	GRID A100A NVIDIA	32 GB	1,870 GB/s	May 2020	25.4 GB	Q6_K	Tight
194 tok/s 116–310 · low confidence	GeForce RTX 5090 NVIDIA	32 GB	1,790 GB/s	Jan 2025	25.4 GB	Q6_K	Tight
194 tok/s 116–310 · low confidence	GeForce RTX 5090 D NVIDIA	32 GB	1,790 GB/s	Jan 2025	25.4 GB	Q6_K	Tight
193 tok/s 116–309 · low confidence	GeForce RTX 5080 NVIDIA	16 GB	960 GB/s	Jan 2025	14.4 GB	Q3_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: NVIDIA
Organisation type: Industry
Country: United States of America
Published: 15 December 2025
Authors: Abhinav Khattar, Aleksander Ficek, Alisa Liu, Arham Mehta, Asif Ahamed, Ayush Dattagupta, Benedikt Schifferer, Brandon Norick, Branislav Kisacanin, Dan Su, Dane Corneil, Daria Gitman, Dhruv Nathawani, Dima Rekesh, Divyanshu Kakwani, Edgar Minasyan, Eileen Long, Ellie Evans, Eric Tramel, Evelina Bakhturina, Felipe Soares, Feng Chen, Gantavya Bhatt, George Armstrong, Igor Gitman, Ivan Moshkov, Jane …

What it does

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

Domain: Language
Task: Language modeling/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: 31.6B
Training data: 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: 4.8 × 10²³ FLOP

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 H100 SXM5 80GB

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)
Hugging Face: nvidia

How it is classified

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

Why it is tracked: Discretionary
Record confidence: Likely

Sources

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

Reference: NVIDIA Nemotron 3 Family of Models
Last updated: 8 April 2026

The extremes

The ten fastest GPUs for Nemotron 3-Nano-30B-A3B

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 Nemotron 3-Nano-30B-A3B

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

Xeon Phi 7120P

Memory needed

14.4 GB

Fastest

596 tok/s

With 31.6B parameters, Nemotron 3-Nano-30B-A3B 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 least hardware that works is a Xeon Phi 7120P. Its 16 GB is enough at Q3_K_M compression, giving roughly 46.0 tokens per second.

Top of the range is the B200, at roughly 596 tokens per second thanks to 8,000 GB/s of bandwidth.

Background

Nemotron 3-Nano-30B-A3B was published by NVIDIA, in United States of America, in December 2025. industry is the category the publisher falls under.

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

The weights being open is what puts this page in the calculator rather than only in the catalogue: it is a model you can actually hold. It is published under the nvidia organisation on Hugging Face.

Reading the throughput figures

Across every card that can run it, the middle of the range is about 94.0 tokens per second, and 239 of them clear the ten tokens per second that roughly matches reading speed.

Because it routes each token through a subset of its weights, it produces text at the pace of a much smaller model. The catch is memory: all of it still has to fit, so the speed is a bonus rather than a discount on hardware.

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

Training it took roughly 4.8 × 10²³ FLOP of computation, on NVIDIA H100 SXM5 80GB — a measure of what producing the model cost, not of how fast it answers.

The reason it appears in this catalogue at all is discretionary.

Step by step

How to choose a GPU for Nemotron 3-Nano-30B-A3B

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 Nemotron 3-Nano-30B-A3B — around 14.4 GB at Q3_K_M. Capacity is the gate — a card either holds it or it does not.
02

Match the context to your actual use

Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason Nemotron 3-Nano-30B-A3B stops fitting a card that seemed fine.
03

Decide how much compression you will accept

The quantisation column varies by card, because a bigger card holds a more accurate copy of Nemotron 3-Nano-30B-A3B — Q3_K_M on the smallest card that fits. Set a floor to hold the comparison at one level.
04

Rank by throughput rather than spec sheet

The speed ordering for Nemotron 3-Nano-30B-A3B is effectively an ordering by memory bandwidth, which is why the B200 tops it at 596 tok/s.
05

Check the fit verdict before buying

The fit column separates cards that just manage Nemotron 3-Nano-30B-A3B from those with room to spare. Buy for the second if the context might grow.
06

See what else that card runs

Every card name links to its own page, which runs the same calculation across the whole model catalogue. Worth a look before buying for Nemotron 3-Nano-30B-A3B alone — a card is usually bought for more than one model.

Answers

Nemotron 3-Nano-30B-A3B — common questions

When was Nemotron 3-Nano-30B-A3B released?

Nemotron 3-Nano-30B-A3B was published in December 2025.

What is Nemotron 3-Nano-30B-A3B used for?

Nemotron 3-Nano-30B-A3B works in Language, and is recorded as handling language modeling/generation. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download Nemotron 3-Nano-30B-A3B?

Its weights are published under the nvidia 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 Nemotron 3-Nano-30B-A3B?

Around 4.8 × 10²³ FLOP, on NVIDIA H100 SXM5 80GB. 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 Nemotron 3-Nano-30B-A3B 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 7.3 GB. Our figures for Nemotron 3-Nano-30B-A3B assume it is fully resident.

Would two GPUs run Nemotron 3-Nano-30B-A3B faster?

Two cards buy memory rather than speed. That matters for Nemotron 3-Nano-30B-A3B only if one card cannot hold it — 241 can, so a second adds little.

Why does the quantisation differ between cards for Nemotron 3-Nano-30B-A3B?

Because capacity varies, so does how hard Nemotron 3-Nano-30B-A3B has to be squeezed — 6 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these Nemotron 3-Nano-30B-A3B speed estimates?

Every figure is derived from memory bandwidth and model size, not benchmarked. That is why each is published as a range such as 357–953 tok/s on the B200 rather than a single number.

What GPU do I need to run Nemotron 3-Nano-30B-A3B?

The smallest card in our catalogue that holds Nemotron 3-Nano-30B-A3B is the Xeon Phi 7120P, with 16 GB of memory. It runs the model at Q3_K_M using about 14.4 GB, and produces roughly 46.0 tokens per second. 241 cards in total can run it.

How fast is Nemotron 3-Nano-30B-A3B on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 596 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 239 of the cards that can run Nemotron 3-Nano-30B-A3B clear that.

How much VRAM does Nemotron 3-Nano-30B-A3B need?

About 14.4 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 Nemotron 3-Nano-30B-A3B on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q3_K_M, using about 14.4 GB and generating roughly 227 tokens per second — a tight fit.

Can I run Nemotron 3-Nano-30B-A3B on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q4_K_M, using about 18.1 GB and generating roughly 230 tokens per second — a tight fit.

Is Nemotron 3-Nano-30B-A3B open source?

Its weights are published, so Nemotron 3-Nano-30B-A3B 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 Nemotron 3-Nano-30B-A3B have?

Nemotron 3-Nano-30B-A3B has 31.6B parameters. "Nano 30B-A3B Base contains 31.6B total parameters out of which 3.2B are activ". 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 Nemotron 3-Nano-30B-A3B?

Nemotron 3-Nano-30B-A3B was published by NVIDIA, based in United States of America, categorised as industry.

Source

Original publication

Record last updated 8 April 2026

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