EXAONE 4.0 (32B) TPS calculator

Open weights LG AI Research 32B parameters July 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

132 cards that can run it

818 cards we hold specifications for

Smallest card that fits

RTX A4500

20 GB · Q3_K_M · 22.9 tok/s

Fastest card

B200

106 tok/s · 180 GB

Which GPUs can run EXAONE 4.0 (32B)?

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
106 tok/s 64–169 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	35.0 GB	Q8_0	Comfortable
106 tok/s 64–169 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	35.0 GB	Q8_0	Comfortable
84.6 tok/s 51–135 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	35.0 GB	Q8_0	Comfortable
84.6 tok/s 51–135 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	35.0 GB	Q8_0	Comfortable
67.6 tok/s 41–108 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	35.0 GB	Q8_0	Comfortable
64.7 tok/s 39–104 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	35.0 GB	Q8_0	Comfortable
64.7 tok/s 39–104 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	35.0 GB	Q8_0	Comfortable
61.9 tok/s 37–99 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	35.0 GB	Q8_0	Comfortable
55.0 tok/s 33–88 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	35.0 GB	Q8_0	Comfortable
55.0 tok/s 33–88 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	35.0 GB	Q8_0	Comfortable
55.0 tok/s 33–88 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	35.0 GB	Q8_0	Comfortable
52.2 tok/s 31–83 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	35.0 GB	Q8_0	Comfortable
44.5 tok/s 27–71 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	35.0 GB	Q8_0	Comfortable
44.5 tok/s 27–71 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	35.0 GB	Q8_0	Comfortable
44.5 tok/s 27–71 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	35.0 GB	Q8_0	Comfortable
44.5 tok/s 27–71 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	35.0 GB	Q8_0	Comfortable
44.5 tok/s 27–71 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	35.0 GB	Q8_0	Comfortable
40.9 tok/s 25–66 · low confidence	GeForce RTX 5090 D V2 NVIDIA	24 GB	1,340 GB/s	Aug 2025	20.1 GB	Q4_K_M	Tight
37.3 tok/s 22–60 · low confidence	A30X NVIDIA	24 GB	1,220 GB/s	Apr 2021	20.1 GB	Q4_K_M	Tight
36.0 tok/s 22–58 · low confidence	DRIVE A100 PROD NVIDIA	32 GB	1,870 GB/s	May 2020	27.5 GB	Q6_K	Tight
36.0 tok/s 22–58 · low confidence	GRID A100A NVIDIA	32 GB	1,870 GB/s	May 2020	27.5 GB	Q6_K	Tight
34.4 tok/s 21–55 · low confidence	GeForce RTX 5090 NVIDIA	32 GB	1,790 GB/s	Jan 2025	27.5 GB	Q6_K	Tight
34.4 tok/s 21–55 · low confidence	GeForce RTX 5090 D NVIDIA	32 GB	1,790 GB/s	Jan 2025	27.5 GB	Q6_K	Tight
33.9 tok/s 20–54 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	35.0 GB	Q8_0	Comfortable
33.9 tok/s 20–54 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	35.0 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: LG AI Research
Organisation type: Industry
Country: Korea (Republic of)
Published: 15 July 2025
Authors: LG AI Research: Kyunghoon Bae, Eunbi Choi, Kibong Choi, Stanley Jungkyu Choi, Yemuk Choi, Kyubeen Han, Seokhee Hong, Junwon Hwang, Taewan Hwang, Joonwon Jang, Hyojin Jeon, Kijeong Jeon, Gerrard Jeongwon Jo, Hyunjik Jo, Jiyeon Jung, Euisoon Kim, Hyosang Kim, Jihoon Kim, Joonkee Kim, Seonghwan Kim, Soyeon Kim, Sunkyoung Kim, Yireun Kim, Yongil Kim, Youchul Kim, Edward Hwayoung Lee, Gwangho Lee, Haej…

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, Code generation, Quantitative reasoning, Translation
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: 32B
Training data: 14,000,000,000,000 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: 2.7 × 10²⁴ FLOP
How it was established: Reported

The training run

What it physically took to train: which chips, how many, for how long, and what that drew from the wall.

Training hardware: NVIDIA H200 SXM
Chips used: 512
Wall-clock time: 3,192 hours (133 days)
Power draw: 701.4 kW

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: LGAI-EXAONE

How it is classified

Labels the source dataset applies when tracking notable models, and how confident it is in the entry.

Why it is tracked: Training cost
Record confidence: Confident

Sources

Where this record came from and when it was last checked.

Reference: EXAONE 4.0: Unified Large Language Models Integrating Non-reasoning and Reasoning Modes
Last updated: 11 February 2026

The extremes

The ten fastest GPUs that run EXAONE 4.0 (32B)

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 EXAONE 4.0 (32B)

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

RTX A4500

Memory needed

16.3 GB

Fastest

106 tok/s

With 32B parameters, EXAONE 4.0 (32B) 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 least hardware that works is a RTX A4500. Its 20 GB is enough at Q3_K_M compression, giving roughly 22.9 tokens per second.

At the other end, a B200 generates roughly 106 tokens per second on it, on the strength of 8,000 GB/s of memory bandwidth.

About this model

EXAONE 4.0 (32B) was published by LG AI Research, in Korea (Republic of), in July 2025. industry is the category the publisher falls under.

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

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

How fast it runs, and why

Half the cards that hold it manage more than 20.7 tokens per second, and 103 exceed reading speed outright.

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.

Training and provenance

The training run consumed about 2.7 × 10²⁴ FLOP, on NVIDIA H200 SXM. That figure describes the cost of creating it and has no bearing on how quickly it generates text.

The training set ran to roughly 14,000,000,000,000 tokens.

Its inclusion criterion is training cost.

Step by step

How to choose a GPU for EXAONE 4.0 (32B)

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

The table lists every card that can hold EXAONE 4.0 (32B) — around 16.3 GB at Q3_K_M. That figure, not the card's headline performance, is what decides whether it runs.
02

Set the context length you will work at

The conversation occupies memory too, and grows as it goes. Set the slider to the length you expect: at long context EXAONE 4.0 (32B) can slip off a card that handles short questions easily.
03

Set a quality floor

The quantisation column varies by card, because a bigger card holds a more accurate copy of EXAONE 4.0 (32B) — Q3_K_M on the smallest card that fits. Set a floor to hold the comparison at one level.
04

Sort by speed

Sort by speed to see how cards rank for EXAONE 4.0 (32B). It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 106 tok/s.
05

Check the fit verdict before buying

The fit column separates cards that just manage EXAONE 4.0 (32B) from those with room to spare. Buy for the second if the context might grow.
06

Open the card you have settled on

Following a card through to its own page shows every other model it can hold, which is the question that follows once EXAONE 4.0 (32B) is settled.

Answers

EXAONE 4.0 (32B) — common questions

Where can I download EXAONE 4.0 (32B)?

Its weights are published under the LGAI-EXAONE 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 EXAONE 4.0 (32B)?

Around 2.7 × 10²⁴ FLOP, on NVIDIA H200 SXM. 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 EXAONE 4.0 (32B) 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 EXAONE 4.0 (32B) is rarely worth using — the nearest miss we calculate is short by 5.7 GB. Every figure here assumes the whole model is on the card.

Would two GPUs run EXAONE 4.0 (32B) faster?

Capacity adds across cards; throughput does not. Since 132 of the cards we track already hold EXAONE 4.0 (32B) on their own, a second card is rarely the answer here.

Why does the quantisation differ between cards for EXAONE 4.0 (32B)?

Because capacity varies, so does how hard EXAONE 4.0 (32B) has to be squeezed — 5 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these EXAONE 4.0 (32B) speed estimates?

They are calculated from specifications rather than measured, and each carries a range — 64–169 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 EXAONE 4.0 (32B)?

The smallest card in our catalogue that holds EXAONE 4.0 (32B) is the RTX A4500, with 20 GB of memory. It runs the model at Q3_K_M using about 16.3 GB, and produces roughly 22.9 tokens per second. 132 cards in total can run it.

How fast is EXAONE 4.0 (32B) on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 106 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 EXAONE 4.0 (32B) clear that.

How much VRAM does EXAONE 4.0 (32B) need?

About 16.3 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 EXAONE 4.0 (32B) on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q4_K_M, using about 20.1 GB and generating roughly 40.9 tokens per second — a tight fit.

Is EXAONE 4.0 (32B) open source?

Its weights are published, so EXAONE 4.0 (32B) 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 EXAONE 4.0 (32B) have?

EXAONE 4.0 (32B) has 32B parameters. 32B. 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 EXAONE 4.0 (32B)?

EXAONE 4.0 (32B) was published by LG AI Research, based in Korea (Republic of), categorised as industry.

When was EXAONE 4.0 (32B) released?

EXAONE 4.0 (32B) was published in July 2025.

What is EXAONE 4.0 (32B) used for?

EXAONE 4.0 (32B) works in Language, and is recorded as handling language modeling/generation, Question answering, Code generation, Quantitative reasoning, Translation. These are the areas it was designed around; they describe intent rather than a hard boundary.

Source

Original publication

Record last updated 11 February 2026

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