EXAONE 3.5 2.4B TPS calculator

Open weights LG AI Research 2.4B parameters December 2024

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

818 cards that can run it

818 cards we hold specifications for

Smallest card that fits

Tesla C1080

4 GB · Q8_0 · 15.4 tok/s

Fastest card

B200

1,412 tok/s · 180 GB

Which GPUs can run EXAONE 3.5 2.4B?

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

818 cards match

Calculating

					Needs	Quantisation	Fit
1,412 tok/s 847–2,259 · low confidence	B200 NVIDIA	180 GB	8,000 GB/s	Jan 2024	3.3 GB	Q8_0	Comfortable
1,412 tok/s 847–2,259 · low confidence	B300 NVIDIA	288 GB	8,000 GB/s	Sep 2025	3.3 GB	Q8_0	Comfortable
1,127 tok/s 676–1,804 · low confidence	Radeon Instinct MI350X AMD	288 GB	8,190 GB/s	Jan 2025	3.3 GB	Q8_0	Comfortable
1,127 tok/s 676–1,804 · low confidence	Radeon Instinct MI355X AMD	288 GB	8,190 GB/s	Jan 2025	3.3 GB	Q8_0	Comfortable
902 tok/s 541–1,443 · low confidence	Radeon Instinct MI300 AMD	128 GB	6,550 GB/s	Jan 2023	3.3 GB	Q8_0	Comfortable
863 tok/s 518–1,381 · low confidence	H200 NVL NVIDIA	141 GB	4,890 GB/s	Nov 2024	3.3 GB	Q8_0	Comfortable
863 tok/s 518–1,381 · low confidence	H200 SXM 141 GB NVIDIA	141 GB	4,890 GB/s	Nov 2024	3.3 GB	Q8_0	Comfortable
826 tok/s 496–1,321 · low confidence	Radeon Instinct MI325X AMD	256 GB	6,000 GB/s	Oct 2024	3.3 GB	Q8_0	Comfortable
733 tok/s 440–1,173 · low confidence	Radeon Instinct MI300A AMD	128 GB	5,325 GB/s	Dec 2023	3.3 GB	Q8_0	Comfortable
733 tok/s 440–1,173 · low confidence	Radeon Instinct MI300X AMD	192 GB	5,325 GB/s	Dec 2023	3.3 GB	Q8_0	Comfortable
733 tok/s 440–1,173 · low confidence	Radeon Instinct MI308X AMD	192 GB	5,325 GB/s	Dec 2023	3.3 GB	Q8_0	Comfortable
695 tok/s 417–1,112 · low confidence	H100 NVL 94 GB NVIDIA	94 GB	3,940 GB/s	Mar 2023	3.3 GB	Q8_0	Comfortable
593 tok/s 356–949 · low confidence	H100 PCIe 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	3.3 GB	Q8_0	Comfortable
593 tok/s 356–949 · low confidence	H100 SXM5 80 GB NVIDIA	80 GB	3,360 GB/s	Oct 2022	3.3 GB	Q8_0	Comfortable
593 tok/s 356–949 · low confidence	H100 SXM5 94 GB NVIDIA	94 GB	3,360 GB/s	Mar 2023	3.3 GB	Q8_0	Comfortable
593 tok/s 356–949 · low confidence	H100 SXM5 96 GB NVIDIA	96 GB	3,360 GB/s	Mar 2023	3.3 GB	Q8_0	Comfortable
593 tok/s 356–949 · low confidence	H800 SXM5 NVIDIA	80 GB	3,360 GB/s	Mar 2023	3.3 GB	Q8_0	Comfortable
451 tok/s 271–722 · low confidence	Radeon Instinct MI250 AMD	128 GB	3,280 GB/s	Nov 2021	3.3 GB	Q8_0	Comfortable
451 tok/s 271–722 · low confidence	Radeon Instinct MI250X AMD	128 GB	3,280 GB/s	Nov 2021	3.3 GB	Q8_0	Comfortable
376 tok/s 226–602 · low confidence	Data Center GPU Max 1550 Intel	128 GB	3,280 GB/s	Jan 2023	3.3 GB	Q8_0	Comfortable
368 tok/s 221–589 · low confidence	Data Center GPU Max Subsystem Intel	128 GB	3,210 GB/s	Jan 2023	3.3 GB	Q8_0	Comfortable
360 tok/s 216–576 · low confidence	A100 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Nov 2020	3.3 GB	Q8_0	Comfortable
360 tok/s 216–576 · low confidence	A100X NVIDIA	80 GB	2,040 GB/s	Jun 2021	3.3 GB	Q8_0	Comfortable
360 tok/s 216–576 · low confidence	A800 SXM4 80 GB NVIDIA	80 GB	2,040 GB/s	Aug 2022	3.3 GB	Q8_0	Comfortable
360 tok/s 216–576 · low confidence	H100 CNX NVIDIA	80 GB	2,040 GB/s	Mar 2023	3.3 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: 9 December 2024
Authors: Soyoung An, Kyunghoon Bae, Eunbi Choi, Kibong Choi, Stanley Jungkyu Choi, Seokhee Hong, Junwon Hwang, Hyojin Jeon, Gerrard Jeongwon Jo, Hyunjik Jo, Jiyeon Jung, Yountae Jung, Hyosang Kim, Joonkee Kim, Seonghwan Kim, Soyeon Kim, Sunkyoung Kim, Yireun Kim, Yongil Kim, Youchul Kim, Edward Hwayoung Lee, Haeju Lee, Honglak Lee, Jinsik Lee, Kyungmin Lee, Woohyung Lim, Sangha Park, Sooyoun Park, Yongmin …

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, Translation

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: 2.4B
Training data: 6,500,000,000,000 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: 9.4 × 10²² FLOP
How it was established: Reported

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.

Record confidence: Confident

Sources

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

Reference: EXAONE 3.5: Series of Large Language Models for Real-world Use Cases
Last updated: 28 November 2025

The extremes

The ten fastest GPUs that run EXAONE 3.5 2.4B

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 3.5 2.4B

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

Tesla C1080

Memory needed

3.3 GB

Fastest

1,412 tok/s

EXAONE 3.5 2.4B is small enough at 2.4B parameters that hardware is rarely the obstacle — 818 of the cards we track can run it, including cards several years old.

At the low end, a Tesla C1080 handles it — 4 GB, at Q8_0, for about 15.4 tokens per second.

Top of the range is the B200, at roughly 1,412 tokens per second thanks to 8,000 GB/s of bandwidth.

Background

EXAONE 3.5 2.4B was published by LG AI Research, in Korea (Republic of), in December 2024. It comes out of industry.

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

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 LGAI-EXAONE organisation on Hugging Face.

Reading the throughput figures

Half the cards that hold it manage more than 39.6 tokens per second, and 783 exceed reading speed outright.

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

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.

Training and provenance

The training run consumed about 9.4 × 10²² FLOP. That figure describes the cost of creating it and has no bearing on how quickly it generates text.

It was trained on about 6,500,000,000,000 tokens of text.

Step by step

How to choose a GPU for EXAONE 3.5 2.4B

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

The table lists every card that can hold EXAONE 3.5 2.4B — around 3.3 GB at Q8_0. That figure, not the card's headline performance, is what decides whether it runs.
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 EXAONE 3.5 2.4B.
03

Decide how much compression you will accept

Each card runs the least-compressed copy it can hold — Q8_0 on the smallest card that fits. Setting a floor drops the cards that only manage EXAONE 3.5 2.4B by squeezing it further than you would want.
04

Sort by speed

Sort by speed to see how cards rank for EXAONE 3.5 2.4B. It will not match a gaming ordering — generation is bound by memory bandwidth, which is why the B200 tops it at 1,412 tok/s.
05

Read the fit column last

The fit column separates cards that just manage EXAONE 3.5 2.4B from those with room to spare. Buy for the second if the context might grow.
06

See what else that card runs

Every card name links to its own page, which runs the same calculation across the whole model catalogue. Worth a look before buying for EXAONE 3.5 2.4B alone — a card is usually bought for more than one model.

Answers

EXAONE 3.5 2.4B — common questions

What GPU do I need to run EXAONE 3.5 2.4B?

The smallest card in our catalogue that holds EXAONE 3.5 2.4B is the Tesla C1080, with 4 GB of memory. It runs the model at Q8_0 using about 3.3 GB, and produces roughly 15.4 tokens per second. 818 cards in total can run it.

How fast is EXAONE 3.5 2.4B on a GPU?

It depends on the card. The quickest we calculate is a B200 at about 1,412 tokens per second; the slowest that still runs it manages considerably less. Reading speed is around ten tokens per second, and 783 of the cards that can run EXAONE 3.5 2.4B clear that.

How much VRAM does EXAONE 3.5 2.4B need?

About 3.3 GB at Q8_0 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 3.5 2.4B on a 8 GB GPU?

Yes. A CMP 170HX 8 GB with 8 GB runs it at Q8_0, using about 3.3 GB and generating roughly 263 tokens per second — a comfortable fit.

Can I run EXAONE 3.5 2.4B on a 12 GB GPU?

Yes. A GeForce RTX 3080 Ti with 12 GB runs it at Q8_0, using about 3.3 GB and generating roughly 161 tokens per second — a comfortable fit.

Can I run EXAONE 3.5 2.4B on a 16 GB GPU?

Yes. A Tesla V100 SXM2 16 GB with 16 GB runs it at Q8_0, using about 3.3 GB and generating roughly 199 tokens per second — a comfortable fit.

Can I run EXAONE 3.5 2.4B on a 24 GB GPU?

Yes. A GeForce RTX 5090 D V2 with 24 GB runs it at Q8_0, using about 3.3 GB and generating roughly 236 tokens per second — a comfortable fit.

Is EXAONE 3.5 2.4B open source?

Its weights are published, so EXAONE 3.5 2.4B 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 3.5 2.4B have?

EXAONE 3.5 2.4B has 2.4B parameters. 2.4B. 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 3.5 2.4B?

EXAONE 3.5 2.4B was published by LG AI Research, based in Korea (Republic of), categorised as industry.

When was EXAONE 3.5 2.4B released?

EXAONE 3.5 2.4B was published in December 2024. 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 EXAONE 3.5 2.4B used for?

EXAONE 3.5 2.4B works in Language, and is recorded as handling language modeling/generation, Question answering, Translation. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.

Where can I download EXAONE 3.5 2.4B?

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 3.5 2.4B?

Around 9.4 × 10²² 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 EXAONE 3.5 2.4B 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 3.5 2.4B is rarely worth using. Every figure here assumes the whole model is on the card.

Would two GPUs run EXAONE 3.5 2.4B faster?

Two cards buy memory rather than speed. That matters for EXAONE 3.5 2.4B only if one card cannot hold it — 818 can, so a second adds little.

Why does the quantisation differ between cards for EXAONE 3.5 2.4B?

Because capacity varies, so does how hard EXAONE 3.5 2.4B has to be squeezed — 1 distinct levels appear in the table above. Set a minimum quality to compare at one.

How accurate are these EXAONE 3.5 2.4B 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 847–2,259 tok/s on the B200 rather than a single number.

Source

Original publication

Record last updated 28 November 2025

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