Nemotron-H 47B TPS calculator

Open weights NVIDIA 47B parameters April 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

93 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Radeon PRO V710

28 GB · Q3_K_M · 9.6 tok/s

Fastest card

B200

72.1 tok/s · 180 GB

Which GPUs can run Nemotron-H 47B?

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

93 cards match

Calculating

					Needs	Quantisation	Fit
72.1 tok/s 43–115 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	51.0 GB	Q8_0	Comfortable
72.1 tok/s 43–115 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	51.0 GB	Q8_0	Comfortable
57.6 tok/s 35–92 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	51.0 GB	Q8_0	Comfortable
57.6 tok/s 35–92 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	51.0 GB	Q8_0	Comfortable
46.0 tok/s 28–74 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	51.0 GB	Q8_0	Comfortable
44.1 tok/s 26–71 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	51.0 GB	Q8_0	Comfortable
44.1 tok/s 26–71 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	51.0 GB	Q8_0	Comfortable
42.2 tok/s 25–67 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	51.0 GB	Q8_0	Comfortable
41.4 tok/s 25–66 · low confidence	DRIVE A100 PROD NVIDIA	32 GB	1,870 GB/s	May 2020	26.4 GB	IQ4_XS	Tight
41.4 tok/s 25–66 · low confidence	GRID A100A NVIDIA	32 GB	1,870 GB/s	May 2020	26.4 GB	IQ4_XS	Tight
39.6 tok/s 24–63 · low confidence	GeForce RTX 5090 NVIDIA	32 GB	1,790 GB/s	Jan 2025	26.4 GB	IQ4_XS	Tight
39.6 tok/s 24–63 · low confidence	GeForce RTX 5090 D NVIDIA	32 GB	1,790 GB/s	Jan 2025	26.4 GB	IQ4_XS	Tight
37.4 tok/s 22–60 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	51.0 GB	Q8_0	Comfortable
37.4 tok/s 22–60 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	51.0 GB	Q8_0	Comfortable
37.4 tok/s 22–60 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	51.0 GB	Q8_0	Comfortable
35.5 tok/s 21–57 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	51.0 GB	Q8_0	Comfortable
30.3 tok/s 18–48 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	51.0 GB	Q8_0	Comfortable
30.3 tok/s 18–48 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	51.0 GB	Q8_0	Comfortable
30.3 tok/s 18–48 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	51.0 GB	Q8_0	Comfortable
30.3 tok/s 18–48 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	51.0 GB	Q8_0	Comfortable
30.3 tok/s 18–48 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	51.0 GB	Q8_0	Comfortable
25.1 tok/s 15–40 · low confidence	A100 PCIe 40 GB NVIDIA	40 GB	1,560 GB/s	Jun 2020	34.6 GB	Q5_K_M	Tight
25.1 tok/s 15–40 · low confidence	A100 SXM4 40 GB NVIDIA	40 GB	1,560 GB/s	May 2020	34.6 GB	Q5_K_M	Tight
25.1 tok/s 15–40 · low confidence	A800 PCIe 40 GB NVIDIA	40 GB	1,560 GB/s	Nov 2022	34.6 GB	Q5_K_M	Tight
25.0 tok/s 15–40 · low confidence	Tesla PG500-216 NVIDIA	32 GB	1,130 GB/s	Nov 2019	26.4 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: NVIDIA
Organisation type: Industry
Country: United States of America
Published: 14 April 2025
Authors: NVIDIA: Aaron Blakeman, Aarti Basant, Abhinav Khattar, Adithya Renduchintala, Akhiad Bercovich, Aleksander Ficek, Alexis Bjorlin, Ali Taghibakhshi, Amala Sanjay Deshmukh, Ameya Sunil Mahabaleshwarkar, Andrew Tao, Anna Shors, Ashwath Aithal, Ashwin Poojary, Ayush Dattagupta, Balaram Buddharaju, Bobby Chen, Boris Ginsburg, Boxin Wang, Brandon Norick, Brian Butterfield, Bryan Catanzaro, Carlo del Mun…

What it does

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

Domain: Language
Task: Language modeling/generation, Question answering, Translation, Quantitative reasoning, Code generation, Neural Architecture Search - NAS
Base model: Nemotron-H 56B
Numerical format: FP8

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: 47B
Training data: tokens
Epochs: 1

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.

How it was established: Operation counting
Fine-tuning compute: 1.8 × 10²² FLOP

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 (non-commercial)
Training code: Unreleased
Hugging Face: nvidia

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: Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Nemotron-H 47B

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-H 47B

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

Radeon PRO V710

Memory needed

23.7 GB

Fastest

72.1 tok/s

Nemotron-H 47B reaches a parameter count of 47B. That lands in the range a serious desktop card can handle once the weights are compressed. The number of cards we track that can run it: 93.

The least hardware that works is Radeon PRO V710, with a memory capacity of 28 GB, running it at a compression of Q3_K_M and producing around 9.6 tokens per second.

Top of the range is B200, generating roughly 72.1 tokens per second on the strength of a memory bandwidth of 8,000 GB/s.

About this model

Nemotron-H 47B was published by NVIDIA, in the country recorded as United States of America, during April 2025. It comes out of an organisation categorised as industry.

It works in the domain of Language, and is recorded as performing the task of language modeling/generation, Question answering, Translation, Quantitative reasoning, Code generation, Neural Architecture Search - NAS.

Its starting point was an existing base model, Nemotron-H 56B. Most models at this scale are adapted from an existing base rather than built from nothing.

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

How fast it runs, and why

The median result is around 18.2 tokens per second. Clearing the ten tokens per second that roughly matches reading speed: 72 of them.

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.

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.

Step by step

How to choose a GPU for Nemotron-H 47B

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 Nemotron-H 47B, needing around 23.7 GB at a compression of Q3_K_M. 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 a card that seemed fine stops fitting Nemotron-H 47B.
03

Choose how far you will compress it

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

Compare tokens per second, not specifications

The speed ordering is effectively an ordering by memory bandwidth, for Nemotron-H 47B. It will not match a gaming ordering, because generation is bound by memory bandwidth. The card topping the list is B200, at 72.1 tok/s.
05

Look at the headroom, not just the fit

A tight fit runs, but leaves nothing spare for a longer conversation, in the case of Nemotron-H 47B. 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

See what else that card runs

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 Nemotron-H 47B.

Answers

Nemotron-H 47B — common questions

Nemotron-H 47B— would two GPUs run it faster?

Two cards buy memory rather than speed, which matters only if one card cannot hold it. The number that can: 93. So a second card is rarely the answer here.

Nemotron-H 47B— 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: 5. Bigger cards get the more accurate version, and the quality floor above pins the comparison to one level.

Nemotron-H 47B— how accurate are these speed estimates?

They are calculated from specifications rather than measured, and each carries a range. One example: 43–115 tok/s on B200. The same model and card vary by thirty to fifty per cent depending on the inference software and its version.

Nemotron-H 47B— what GPU do I need to run it?

The smallest card in our catalogue that holds it is Radeon PRO V710, with a memory capacity of 28 GB. It runs the model at a compression of Q3_K_M using about 23.7 GB, and produces roughly 9.6 tokens per second. The number of cards able to run it in total: 93.

Nemotron-H 47B— how fast is it on a GPU?

It depends on the card. The quickest we calculate is B200, at about 72.1 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: 72.

Nemotron-H 47B— how much VRAM does it need?

It needs about 23.7 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.

Nemotron-H 47B— 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.

Nemotron-H 47B— how many parameters does it have?

It has a parameter count of 47B. "We use MiniPuzzle to distill Nemotron-H-56B-Base to Nemotron-H-47B-Base, using only 63 billion training tokens and FP8 training". 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.

Nemotron-H 47B— who created it?

It was published by NVIDIA, based in United States of America, an organisation categorised as industry.

Nemotron-H 47B— when was it released?

It was published in April 2025.

Nemotron-H 47B— what is it used for?

It works in the domain of Language, and is recorded as handling the task of language modeling/generation, Question answering, Translation, Quantitative reasoning, Code generation, Neural Architecture Search - NAS. These are the areas it was designed around; they describe intent rather than a hard boundary.

Nemotron-H 47B— where can I download it?

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

Nemotron-H 47B— can I run it if it does not fit in my GPU?

It can be split between the card and system memory, but it generates painfully slowly that way. The nearest miss we calculate falls short by 7.5 GB. Every figure here assumes the whole model is resident on the card.

Source

Original publication

Record last updated 28 November 2025

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