Llama-Primus-Nemotron-70B TPS calculator

Open weights Trend Micro 70B 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

61 cards that can run it

818 cards we hold specifications for

Smallest card that fits

A100 PCIe 40 GB

40 GB · Q3_K_M · 25.5 tok/s

Fastest card

B200

48.4 tok/s · 180 GB

Which GPUs can run Llama-Primus-Nemotron-70B?

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

61 cards match

Calculating

					Needs	Quantisation	Fit
48.4 tok/s 41–58	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	73.8 GB	Q8_0	Comfortable
48.4 tok/s 41–58	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	73.8 GB	Q8_0	Comfortable
38.7 tok/s 23–62 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	73.8 GB	Q8_0	Comfortable
38.7 tok/s 23–62 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	73.8 GB	Q8_0	Comfortable
30.9 tok/s 19–49 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	73.8 GB	Q8_0	Comfortable
29.6 tok/s 25–36	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	73.8 GB	Q8_0	Comfortable
29.6 tok/s 25–36	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	73.8 GB	Q8_0	Comfortable
29.5 tok/s 25–35	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	57.5 GB	Q6_K	Comfortable
29.5 tok/s 25–35	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	57.5 GB	Q6_K	Comfortable
28.3 tok/s 17–45 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	73.8 GB	Q8_0	Comfortable
26.1 tok/s 22–31	GRID A100B NVIDIA	48 GB	1,870 GB/s	May 2020	41.2 GB	Q4_K_M	Tight
25.5 tok/s 22–31	A100 PCIe 40 GB NVIDIA	40 GB	1,560 GB/s	Jun 2020	33.0 GB	Q3_K_M	Tight
25.5 tok/s 22–31	A100 SXM4 40 GB NVIDIA	40 GB	1,560 GB/s	May 2020	33.0 GB	Q3_K_M	Tight
25.5 tok/s 22–31	A800 PCIe 40 GB NVIDIA	40 GB	1,560 GB/s	Nov 2022	33.0 GB	Q3_K_M	Tight
25.1 tok/s 15–40 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	73.8 GB	Q8_0	Comfortable
25.1 tok/s 15–40 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	73.8 GB	Q8_0	Comfortable
25.1 tok/s 15–40 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	73.8 GB	Q8_0	Comfortable
23.8 tok/s 20–29	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	73.8 GB	Q8_0	Tight
20.3 tok/s 17–24	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	73.8 GB	Q8_0	Tight
20.3 tok/s 17–24	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	73.8 GB	Q8_0	Tight
20.3 tok/s 17–24	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	73.8 GB	Q8_0	Tight
18.7 tok/s 16–22	RTX PRO 5000 Blackwell NVIDIA	48 GB	1,340 GB/s	Mar 2025	41.2 GB	Q4_K_M	Tight
17.9 tok/s 15–22	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	57.5 GB	Q6_K	Comfortable
17.9 tok/s 15–22	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	57.5 GB	Q6_K	Comfortable
17.9 tok/s 15–22	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	57.5 GB	Q6_K	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: Trend Micro
Organisation type: Industry
Country: Japan, United States of America
Published: 21 April 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
Base model: Llama-3.1-Nemotron-70B-Instruct

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: 70B
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.

How it was established: Operation counting
Fine-tuning compute: 4.2 × 10²¹ 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 (restricted use)
Training code: Unreleased
Hugging Face: trend-cybertron

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: Llama-Primus-Nemotron-70B-Base
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Llama-Primus-Nemotron-70B

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 Llama-Primus-Nemotron-70B

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

The hardware side

Minimum card

A100 PCIe 40 GB

Memory needed

33.0 GB

Fastest

48.4 tok/s

Llama-Primus-Nemotron-70B sits at 70B parameters, which puts it above consumer hardware and into the range where a card is bought for this purpose rather than repurposed for it. 61 of the cards we track can hold it.

At the low end, a A100 PCIe 40 GB handles it — 40 GB, at Q3_K_M, for about 25.5 tokens per second.

The quickest result comes from a B200 at around 48.4 tokens per second — its 8,000 GB/s of bandwidth is what buys that.

About this model

Llama-Primus-Nemotron-70B was published by Trend Micro, in Japan, in April 2025. The organisation is categorised as industry.

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

It is derived from Llama-3.1-Nemotron-70B-Instruct rather than trained from scratch, which is the usual way a specialised model is produced.

Published weights mean the model runs on your machine rather than someone else's, which is what makes the hardware question below answerable at all. It is published under the trend-cybertron organisation on Hugging Face.

How fast it runs, and why

Across every card that can run it, the middle of the range is about 17.1 tokens per second, and 49 of them clear the ten tokens per second that roughly matches reading speed.

Every weight participates in every token here, so bandwidth is the whole story: the ranking below is effectively a ranking of memory throughput.

Its attention layout is on file, so the memory figures are computed exactly rather than approximated.

Step by step

How to choose a GPU for Llama-Primus-Nemotron-70B

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 Llama-Primus-Nemotron-70B actually needs — around 33.0 GB at Q3_K_M. No amount of processing power compensates for a card that cannot hold it.
02

Decide how long your conversations run

Set the context to what you will actually use. The cache grows with the conversation, and it is the usual reason Llama-Primus-Nemotron-70B 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 — Q3_K_M on the smallest card that fits. Setting a floor drops the cards that only manage Llama-Primus-Nemotron-70B by squeezing it further than you would want.
04

Compare tokens per second, not specifications

Ranking by tokens per second for Llama-Primus-Nemotron-70B follows memory bandwidth, not core counts, which is why the B200 tops it at 48.4 tok/s.
05

Look at the headroom, not just the fit

Tight means Llama-Primus-Nemotron-70B 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

Check the card from the other side

Following a card through to its own page shows every other model it can hold, which is the question that follows once Llama-Primus-Nemotron-70B is settled.

Answers

Llama-Primus-Nemotron-70B — common questions

Would two GPUs run Llama-Primus-Nemotron-70B faster?

Two cards buy memory rather than speed. That matters for Llama-Primus-Nemotron-70B only if one card cannot hold it — 61 can, so a second adds little.

Why does the quantisation differ between cards for Llama-Primus-Nemotron-70B?

A larger card holds a more accurate copy. Across the cards that run Llama-Primus-Nemotron-70B, 4 compression levels are used; the floor control above pins it to one.

How accurate are these Llama-Primus-Nemotron-70B speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 41–58 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.

What GPU do I need to run Llama-Primus-Nemotron-70B?

The smallest card in our catalogue that holds Llama-Primus-Nemotron-70B is the A100 PCIe 40 GB, with 40 GB of memory. It runs the model at Q3_K_M using about 33.0 GB, and produces roughly 25.5 tokens per second. 61 cards in total can run it.

How fast is Llama-Primus-Nemotron-70B on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 48.4 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 49 of the cards that can run Llama-Primus-Nemotron-70B clear that.

How much VRAM does Llama-Primus-Nemotron-70B need?

About 33.0 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.

Is Llama-Primus-Nemotron-70B open source?

Its weights are published, so Llama-Primus-Nemotron-70B 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 Llama-Primus-Nemotron-70B have?

Llama-Primus-Nemotron-70B has 70B parameters. 70b. 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 Llama-Primus-Nemotron-70B?

Llama-Primus-Nemotron-70B was published by Trend Micro, based in Japan, categorised as industry.

When was Llama-Primus-Nemotron-70B released?

Llama-Primus-Nemotron-70B was published in April 2025.

What is Llama-Primus-Nemotron-70B used for?

Llama-Primus-Nemotron-70B works in Language, and is recorded as handling language modeling/generation, Question answering. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download Llama-Primus-Nemotron-70B?

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

Can I run Llama-Primus-Nemotron-70B if it does not fit in my GPU?

Partly. Layers that do not fit sit in system memory and run at a fraction of the speed, so a mostly-offloaded Llama-Primus-Nemotron-70B is rarely worth using — the nearest miss we calculate is short by 12.4 GB. Every figure here assumes the whole model is on the card.

Source

Original publication

Record last updated 28 November 2025

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