Nous-Hermes-2-Yi-34B TPS calculator

Open weights Nous Research 34B parameters December 2023

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

132 cards that can run it

818 cards we hold specifications for

Smallest card that fits

RTX A4500

20 GB · Q3_K_M · 21.5 tok/s

Fastest card

B200

99.7 tok/s · 180 GB

Which GPUs can run Nous-Hermes-2-Yi-34B?

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

132 cards match

Calculating

					Needs	Quantisation	Fit
99.7 tok/s 60–159 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	37.1 GB	Q8_0	Comfortable
99.7 tok/s 60–159 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	37.1 GB	Q8_0	Comfortable
79.6 tok/s 48–127 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	37.1 GB	Q8_0	Comfortable
79.6 tok/s 48–127 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	37.1 GB	Q8_0	Comfortable
63.6 tok/s 38–102 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	37.1 GB	Q8_0	Comfortable
60.9 tok/s 37–97 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	37.1 GB	Q8_0	Comfortable
60.9 tok/s 37–97 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	37.1 GB	Q8_0	Comfortable
58.3 tok/s 35–93 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	37.1 GB	Q8_0	Comfortable
51.7 tok/s 31–83 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	37.1 GB	Q8_0	Comfortable
51.7 tok/s 31–83 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	37.1 GB	Q8_0	Comfortable
51.7 tok/s 31–83 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	37.1 GB	Q8_0	Comfortable
49.1 tok/s 29–79 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	37.1 GB	Q8_0	Comfortable
41.9 tok/s 25–67 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	37.1 GB	Q8_0	Comfortable
41.9 tok/s 25–67 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	37.1 GB	Q8_0	Comfortable
41.9 tok/s 25–67 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	37.1 GB	Q8_0	Comfortable
41.9 tok/s 25–67 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	37.1 GB	Q8_0	Comfortable
41.9 tok/s 25–67 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	37.1 GB	Q8_0	Comfortable
41.6 tok/s 25–67 · low confidence	DRIVE A100 PROD NVIDIA	32 GB	1,870 GB/s	May 2020	25.2 GB	Q5_K_M	Tight
41.6 tok/s 25–67 · low confidence	GRID A100A NVIDIA	32 GB	1,870 GB/s	May 2020	25.2 GB	Q5_K_M	Tight
39.8 tok/s 24–64 · low confidence	GeForce RTX 5090 NVIDIA	32 GB	1,790 GB/s	Jan 2025	25.2 GB	Q5_K_M	Tight
39.8 tok/s 24–64 · low confidence	GeForce RTX 5090 D NVIDIA	32 GB	1,790 GB/s	Jan 2025	25.2 GB	Q5_K_M	Tight
38.5 tok/s 23–62 · low confidence	GeForce RTX 5090 D V2 NVIDIA	24 GB	1,340 GB/s	Aug 2025	21.3 GB	Q4_K_M	Tight
35.1 tok/s 21–56 · low confidence	A30X NVIDIA	24 GB	1,220 GB/s	Apr 2021	21.3 GB	Q4_K_M	Tight
31.9 tok/s 19–51 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	37.1 GB	Q8_0	Comfortable
31.9 tok/s 19–51 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	37.1 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: 25 December 2023

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: Yi-34B

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: 34B
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
Hugging Face: NousResearch

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: Nous Hermes 2 - Yi-34B
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run Nous-Hermes-2-Yi-34B

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 Nous-Hermes-2-Yi-34B

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

Hardware requirements in practice

Minimum card

RTX A4500

Memory needed

17.3 GB

Fastest

99.7 tok/s

With 34B parameters, Nous-Hermes-2-Yi-34B lands in the range a serious desktop card can handle once the weights are compressed. 132 of the cards we track can run it.

The entry point is the RTX A4500: 20 GB of memory, Q3_K_M compression, roughly 21.5 tokens per second.

A B200 is the fastest we calculate for it: about 99.7 tokens per second, from 8,000 GB/s of memory bandwidth.

About this model

Nous-Hermes-2-Yi-34B was published by Nous Research, in United States of America, in December 2023. The organisation is categorised as industry.

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

It builds on Yi-34B, which is why it shares that model's general shape and size.

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 NousResearch organisation on Hugging Face.

How fast it runs, and why

Across every card that can run it, the middle of the range is about 21.2 tokens per second, and 103 of them clear the ten tokens per second that roughly matches reading speed.

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.

Step by step

How to choose a GPU for Nous-Hermes-2-Yi-34B

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

Look at what Nous-Hermes-2-Yi-34B actually needs — around 17.3 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

The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context Nous-Hermes-2-Yi-34B can slip off a card that handles short questions easily.
03

Set a quality floor

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 Nous-Hermes-2-Yi-34B by squeezing it further than you would want.
04

Compare tokens per second, not specifications

The speed ordering for Nous-Hermes-2-Yi-34B is effectively an ordering by memory bandwidth, which is why the B200 tops it at 99.7 tok/s.
05

Look at the headroom, not just the fit

A tight fit runs Nous-Hermes-2-Yi-34B but leaves nothing spare for a longer conversation; comfortable has headroom. If you expect to grow the context, buy for comfortable.
06

Open the card you have settled on

Every card name links to its own page, which runs the same calculation across the whole model catalogue. Worth a look before buying for Nous-Hermes-2-Yi-34B alone — a card is usually bought for more than one model.

Answers

Nous-Hermes-2-Yi-34B — common questions

When was Nous-Hermes-2-Yi-34B released?

Nous-Hermes-2-Yi-34B was published in December 2023. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.

What is Nous-Hermes-2-Yi-34B used for?

Nous-Hermes-2-Yi-34B works in Language, and is recorded as handling language modeling/generation, Question answering. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

Where can I download Nous-Hermes-2-Yi-34B?

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

Can I run Nous-Hermes-2-Yi-34B 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 Nous-Hermes-2-Yi-34B is rarely worth using — the nearest miss we calculate is short by 6.9 GB. Every figure here assumes the whole model is on the card.

Would two GPUs run Nous-Hermes-2-Yi-34B faster?

A second card roughly doubles the memory available but not the generation rate. With 132 cards already able to run Nous-Hermes-2-Yi-34B alone, the case for pairing is weak.

Why does the quantisation differ between cards for Nous-Hermes-2-Yi-34B?

A larger card holds a more accurate copy. Across the cards that run Nous-Hermes-2-Yi-34B, 5 compression levels are used; the floor control above pins it to one.

How accurate are these Nous-Hermes-2-Yi-34B speed estimates?

These are estimates with real error bars. The fastest result here, 60–159 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 Nous-Hermes-2-Yi-34B?

The smallest card in our catalogue that holds Nous-Hermes-2-Yi-34B is the RTX A4500, with 20 GB of memory. It runs the model at Q3_K_M using about 17.3 GB, and produces roughly 21.5 tokens per second. 132 cards in total can run it.

How fast is Nous-Hermes-2-Yi-34B on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 99.7 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 103 of the cards that can run Nous-Hermes-2-Yi-34B clear that.

How much VRAM does Nous-Hermes-2-Yi-34B need?

About 17.3 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 Nous-Hermes-2-Yi-34B on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q4_K_M, using about 21.3 GB and generating roughly 38.5 tokens per second — a tight fit.

Is Nous-Hermes-2-Yi-34B open source?

Its weights are published, so Nous-Hermes-2-Yi-34B 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 Nous-Hermes-2-Yi-34B have?

Nous-Hermes-2-Yi-34B has 34B parameters. 34B. 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 Nous-Hermes-2-Yi-34B?

Nous-Hermes-2-Yi-34B was published by Nous Research, based in United States of America, categorised as industry.

Source

Original publication

Record last updated 28 November 2025

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