Apriel Nemotron 15B TPS calculator

Open weights NVIDIA,ServiceNow 15B parameters May 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

306 cards that can run it

818 cards we hold specifications for

Smallest card that fits

P102-101

10 GB · IQ4_XS · 18.9 tok/s

Fastest card

B200

226 tok/s · 180 GB

Which GPUs can run Apriel Nemotron 15B?

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

306 cards match

Calculating

					Needs	Quantisation	Fit
226 tok/s 136–361 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	16.8 GB	Q8_0	Comfortable
226 tok/s 136–361 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	16.8 GB	Q8_0	Comfortable
180 tok/s 108–289 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	16.8 GB	Q8_0	Comfortable
180 tok/s 108–289 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	16.8 GB	Q8_0	Comfortable
144 tok/s 87–231 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	16.8 GB	Q8_0	Comfortable
138 tok/s 83–221 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	16.8 GB	Q8_0	Comfortable
138 tok/s 83–221 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	16.8 GB	Q8_0	Comfortable
132 tok/s 79–211 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	16.8 GB	Q8_0	Comfortable
117 tok/s 70–188 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	16.8 GB	Q8_0	Comfortable
117 tok/s 70–188 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	16.8 GB	Q8_0	Comfortable
117 tok/s 70–188 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	16.8 GB	Q8_0	Comfortable
111 tok/s 67–178 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	16.8 GB	Q8_0	Comfortable
108 tok/s 65–173 · low confidence	CMP 170HX 10 GB NVIDIA	10 GB	1,560 GB/s	Sep 2021	8.9 GB	IQ4_XS	Tight
94.9 tok/s 57–152 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	16.8 GB	Q8_0	Comfortable
94.9 tok/s 57–152 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	16.8 GB	Q8_0	Comfortable
94.9 tok/s 57–152 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	16.8 GB	Q8_0	Comfortable
94.9 tok/s 57–152 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	16.8 GB	Q8_0	Comfortable
94.9 tok/s 57–152 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	16.8 GB	Q8_0	Comfortable
72.2 tok/s 43–116 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	16.8 GB	Q8_0	Comfortable
72.2 tok/s 43–116 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	16.8 GB	Q8_0	Comfortable
60.2 tok/s 36–96 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	16.8 GB	Q8_0	Comfortable
59.5 tok/s 36–95 · low confidence	GeForce RTX 3080 12 GB NVIDIA	12 GB	912 GB/s	Jan 2022	9.8 GB	Q4_K_M	Tight
59.5 tok/s 36–95 · low confidence	GeForce RTX 3080 Ti NVIDIA	12 GB	912 GB/s	May 2021	9.8 GB	Q4_K_M	Tight
58.9 tok/s 35–94 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	16.8 GB	Q8_0	Comfortable
57.6 tok/s 35–92 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	16.8 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: NVIDIA,ServiceNow
Organisation type: Industry,Industry
Country: United States of America
Published: 6 May 2025

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, Quantitative reasoning, Chat

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: 15B
Training data: 100,000,000,000 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: 9 × 10²¹ 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: ServiceNow-AI

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: New Apriel Nemotron 15B reasoning model delivers lower latency, lower inference costs, and faster agentic AI—purpose built for performance, cost, and scale
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Apriel Nemotron 15B

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 Apriel Nemotron 15B

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 it takes to run this model

Minimum card

P102-101

Memory needed

8.9 GB

Fastest

226 tok/s

Apriel Nemotron 15B is small enough at 15B parameters that hardware is rarely the obstacle — 306 of the cards we track can run it, including cards several years old.

The smallest card that holds it is the P102-101 with 10 GB, running it at IQ4_XS and producing around 18.9 tokens per second.

A B200 is the fastest we calculate for it: about 226 tokens per second, from 8,000 GB/s of memory bandwidth.

About this model

Apriel Nemotron 15B was published by NVIDIA,ServiceNow, in United States of America, in May 2025. It comes out of industry,Industry.

It works in Language, and is recorded as doing language modeling/generation, Question answering, Quantitative reasoning, Chat.

Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded. It is published under the ServiceNow-AI organisation on Hugging Face.

How fast it runs, and why

The median result is around 21.1 tokens per second; 266 cards produce text faster than most people read it.

Being dense, it reads all of itself per token, which is why the ordering by speed below follows the ordering by memory bandwidth so closely.

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 9 × 10²¹ FLOP of arithmetic, which is a statement about the training budget rather than about inference.

Around 100,000,000,000 tokens went into training it.

Step by step

How to choose a GPU for Apriel Nemotron 15B

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 Apriel Nemotron 15B — around 8.9 GB at IQ4_XS. Capacity is the gate — a card either holds it or it does not.
02

Match the context to your actual use

Longer conversations cost memory on top of what the weights need. Move the slider to your real working length before trusting any row for Apriel Nemotron 15B.
03

Set a quality floor

Each card runs the least-compressed copy it can hold — IQ4_XS on the smallest card that fits. Setting a floor drops the cards that only manage Apriel Nemotron 15B by squeezing it further than you would want.
04

Sort by speed

The speed ordering for Apriel Nemotron 15B is effectively an ordering by memory bandwidth, which is why the B200 tops it at 226 tok/s.
05

Look at the headroom, not just the fit

Tight means Apriel Nemotron 15B loads and works, with no room to raise the context later. Comfortable means you can. The difference matters more than a few tokens per second.
06

Open the card you have settled on

Each card page repeats this sweep for every model we hold. It answers what else the hardware is good for, beyond Apriel Nemotron 15B.

Answers

Apriel Nemotron 15B — common questions

Why does the quantisation differ between cards for Apriel Nemotron 15B?

Each card is shown running the least-compressed copy it can hold, and Apriel Nemotron 15B appears at 4 different compression levels across the cards that fit it. Bigger cards get the more accurate version.

How accurate are these Apriel Nemotron 15B speed estimates?

These are estimates with real error bars. The fastest result here, 136–361 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 Apriel Nemotron 15B?

The smallest card in our catalogue that holds Apriel Nemotron 15B is the P102-101, with 10 GB of memory. It runs the model at IQ4_XS using about 8.9 GB, and produces roughly 18.9 tokens per second. 306 cards in total can run it.

How fast is Apriel Nemotron 15B on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 226 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 266 of the cards that can run Apriel Nemotron 15B clear that.

How much VRAM does Apriel Nemotron 15B need?

About 8.9 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 Apriel Nemotron 15B on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q4_K_M, using about 9.8 GB and generating roughly 59.5 tokens per second — a tight fit.

Can I run Apriel Nemotron 15B on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q6_K, using about 13.3 GB and generating roughly 46.4 tokens per second — a tight fit.

Can I run Apriel Nemotron 15B on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 16.8 GB and generating roughly 37.8 tokens per second — a comfortable fit.

Is Apriel Nemotron 15B open source?

Its weights are published, so Apriel Nemotron 15B 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 Apriel Nemotron 15B have?

Apriel Nemotron 15B has 15B parameters. 15B. 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 Apriel Nemotron 15B?

Apriel Nemotron 15B was published by NVIDIA,ServiceNow, based in United States of America, categorised as industry,Industry.

When was Apriel Nemotron 15B released?

Apriel Nemotron 15B was published in May 2025.

What is Apriel Nemotron 15B used for?

Apriel Nemotron 15B works in Language, and is recorded as handling language modeling/generation, Question answering, Quantitative reasoning, Chat. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Where can I download Apriel Nemotron 15B?

Its weights are published under the ServiceNow-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 Apriel Nemotron 15B?

Around 9 × 10²¹ 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 Apriel Nemotron 15B if it does not fit in my GPU?

It can be split between the card and system memory, but Apriel Nemotron 15B generates painfully slowly that way — the nearest miss we calculate is short by 2.6 GB. Nothing on this page assumes offloading.

Would two GPUs run Apriel Nemotron 15B faster?

Capacity adds across cards; throughput does not. Since 306 of the cards we track already hold Apriel Nemotron 15B on their own, a second card is rarely the answer here.

Source

Original publication

Record last updated 28 November 2025

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