Nomos 1 TPS calculator

Open weights Nous Research 30B 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 · 48.4 tok/s

Fastest card

B200

627 tok/s · 180 GB

Which GPUs can run Nomos 1?

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
627 tok/s 376–1,004 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	31.2 GB	Q8_0	Comfortable
627 tok/s 376–1,004 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	31.2 GB	Q8_0	Comfortable
501 tok/s 301–802 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	31.2 GB	Q8_0	Comfortable
501 tok/s 301–802 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	31.2 GB	Q8_0	Comfortable
401 tok/s 240–641 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	31.2 GB	Q8_0	Comfortable
384 tok/s 230–614 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	31.2 GB	Q8_0	Comfortable
384 tok/s 230–614 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	31.2 GB	Q8_0	Comfortable
367 tok/s 220–587 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	31.2 GB	Q8_0	Comfortable
326 tok/s 195–521 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	31.2 GB	Q8_0	Comfortable
326 tok/s 195–521 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	31.2 GB	Q8_0	Comfortable
326 tok/s 195–521 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	31.2 GB	Q8_0	Comfortable
309 tok/s 185–494 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	31.2 GB	Q8_0	Comfortable
264 tok/s 158–422 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	31.2 GB	Q8_0	Comfortable
264 tok/s 158–422 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	31.2 GB	Q8_0	Comfortable
264 tok/s 158–422 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	31.2 GB	Q8_0	Comfortable
264 tok/s 158–422 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	31.2 GB	Q8_0	Comfortable
264 tok/s 158–422 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	31.2 GB	Q8_0	Comfortable
239 tok/s 143–383 · low confidence	Tesla V100 SXM2 16 GB NVIDIA	16 GB	1,130 GB/s	Nov 2019	13.7 GB	Q3_K_M	Tight
213 tok/s 128–341 · low confidence	DRIVE A100 PROD NVIDIA	32 GB	1,870 GB/s	May 2020	24.2 GB	Q6_K	Tight
213 tok/s 128–341 · low confidence	GRID A100A NVIDIA	32 GB	1,870 GB/s	May 2020	24.2 GB	Q6_K	Tight
204 tok/s 122–326 · low confidence	GeForce RTX 5090 NVIDIA	32 GB	1,790 GB/s	Jan 2025	24.2 GB	Q6_K	Tight
204 tok/s 122–326 · low confidence	GeForce RTX 5090 D NVIDIA	32 GB	1,790 GB/s	Jan 2025	24.2 GB	Q6_K	Tight
203 tok/s 122–325 · low confidence	GeForce RTX 5080 NVIDIA	16 GB	960 GB/s	Jan 2025	13.7 GB	Q3_K_M	Tight
201 tok/s 120–321 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	31.2 GB	Q8_0	Comfortable
201 tok/s 120–321 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	31.2 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: Nous Research
Organisation type: Industry
Country: United States of America
Published: 11 December 2025
Authors: Nous Research

What it does

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

Domain: Language
Task: Mathematical problem solving, Proof writing, Reasoning
Base model: Qwen/Qwen3-30B-A3B-Thinking-2507

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: 30B
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)
Training code: Unreleased

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: Nomos 1
Last updated: 15 January 2026

The extremes

The ten fastest GPUs for Nomos 1

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 Nomos 1

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

Xeon Phi 7120P

Memory needed

13.7 GB

Fastest

627 tok/s

With 30B parameters, Nomos 1 lands in the range a serious desktop card can handle once the weights are compressed. 241 of the cards we track can run it.

At the low end, a Xeon Phi 7120P handles it — 16 GB, at Q3_K_M, for about 48.4 tokens per second.

Top of the range is the B200, at roughly 627 tokens per second thanks to 8,000 GB/s of bandwidth.

Where it came from

Nomos 1 was published by Nous Research, in United States of America, in December 2025. It comes out of industry.

It works in Language, and is recorded as doing mathematical problem solving, Proof writing, Reasoning.

It builds on Qwen/Qwen3-30B-A3B-Thinking-2507, which is why it shares that model's general shape and size.

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.

Understanding the speeds

Half the cards that hold it manage more than 95.1 tokens per second, and 239 exceed reading speed outright.

This is a mixture-of-experts model, which routes each token through only part of itself. It therefore generates far faster than its total size suggests — while still needing every parameter resident in memory, so it is quick without being cheap to hold.

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.

Step by step

How to choose a GPU for Nomos 1

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 Nomos 1 actually needs — around 13.7 GB at Q3_K_M. No amount of processing power compensates for a card that cannot hold it.
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 Nomos 1 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 Nomos 1 by squeezing it further than you would want.
04

Compare tokens per second, not specifications

Ranking by tokens per second for Nomos 1 follows memory bandwidth, not core counts, which is why the B200 tops it at 627 tok/s.
05

Look at the headroom, not just the fit

Tight means Nomos 1 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 Nomos 1 is settled.

Answers

Nomos 1 — common questions

Why does the quantisation differ between cards for Nomos 1?

Each card is shown running the least-compressed copy it can hold, and Nomos 1 appears at 5 different compression levels across the cards that fit it. Bigger cards get the more accurate version.

How accurate are these Nomos 1 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 376–1,004 tok/s on the B200 rather than a single number.

What GPU do I need to run Nomos 1?

The smallest card in our catalogue that holds Nomos 1 is the Xeon Phi 7120P, with 16 GB of memory. It runs the model at Q3_K_M using about 13.7 GB, and produces roughly 48.4 tokens per second. 241 cards in total can run it.

How fast is Nomos 1 on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 627 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 Nomos 1 clear that.

How much VRAM does Nomos 1 need?

About 13.7 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 Nomos 1 on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q3_K_M, using about 13.7 GB and generating roughly 239 tokens per second — a tight fit.

Can I run Nomos 1 on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q5_K_M, using about 20.7 GB and generating roughly 188 tokens per second — a tight fit.

Is Nomos 1 open source?

Its weights are published, so Nomos 1 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 Nomos 1 have?

Nomos 1 has 30B parameters. MoE specialization of Qwen3-30B-A3B-Thinking-2507, reported ~3B active parameters. Designed to be used with the open-source Nomos reasoning harness. 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 Nomos 1?

Nomos 1 was published by Nous Research, based in United States of America, categorised as industry.

When was Nomos 1 released?

Nomos 1 was published in December 2025.

What is Nomos 1 used for?

Nomos 1 works in Language, and is recorded as handling mathematical problem solving, Proof writing, Reasoning. These are the areas it was designed around; they describe intent rather than a hard boundary.

Where can I download Nomos 1?

The weights for Nomos 1 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 Nomos 1 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 Nomos 1 is rarely worth using — the nearest miss we calculate is short by 6.4 GB. Every figure here assumes the whole model is on the card.

Would two GPUs run Nomos 1 faster?

A second card roughly doubles the memory available but not the generation rate. With 241 cards already able to run Nomos 1 alone, the case for pairing is weak.

Source

Original publication

Record last updated 15 January 2026

The other direction

Looking at it from the other side?

This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.

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