EXAONE 3.5 2.4B — VRAM, Speed & Local Setup

Written by Jakub Rusinowski · Last updated February 10, 2026

Model library → EXAONE 3.5 → EXAONE 3.5 2.4B

Ultra-compact model for edge devices and phones. Outperforms similarly-sized models on instruction tasks.

EXAONE 3.5 2.4B needs about 2 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.

Specifications

Parameters2.4 Billion
Context window32,768
ArchitectureDense, Decoder-only
ProviderLG AI Research
LicenceMIT
Specified atQ4_K_M
System RAM8 GB
Record updated2026-02-10

Licence

MIT — commercial use permitted. Commercial use permitted. No usage restrictions beyond attribution.

VRAM and Speed by Quantization

Modelled on a reference NVIDIA RTX 4090 (24 GB), at 8K context. Speed figures are ESTIMATES from the memory-bandwidth roofline described on the methodology page, not benchmarks we ran — rows marked measured come from published or reader-submitted runs. VRAM here includes the KV cache, so it reads higher than the headline figure above, which does not.

QuantWeightsVRAM neededEst. speedFit on 24 GB
Q2_K0.8 GB2.2 GB~270 tok/s (est.)Fits comfortably
Q3_K_M1.0 GB2.5 GB~249 tok/s (est.)Fits comfortably
Q4_K_M1.4 GB2.9 GB~218 tok/s (est.)Fits comfortably
Q5_K_M1.7 GB3.1 GB~204 tok/s (est.)Fits comfortably
Q6_K2.0 GB3.4 GB~190 tok/s (est.)Fits comfortably
Q8_02.5 GB4.0 GB~166 tok/s (est.)Fits comfortably
F164.8 GB6.2 GB~111 tok/s (est.)Fits comfortably

Want the memory numbers alone, at every quantization level and your own context length? Use the EXAONE 3.5 2.4B VRAM calculator.

Buy This HardwareIntel Arc B570 10GB — 10 GB VRAM · 150 W board powerDeploy in the Cloud NowRTX 4090 on RunPod — from $0.34/hr · rate checked 2026-07

or compare on Vast.ai from $0.35/hr (typical low · varies)

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Recommended GPU

The cheapest catalogued GPU that runs EXAONE 3.5 2.4B is the Intel Arc B570 (10 GB).

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Intel Arc B570 10GB
10 GB VRAM · 150 W board power
2026 prices are volatile — check the current listing.
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How to Run EXAONE 3.5 2.4B

Install Ollama, then run:

ollama run exaone3.5:2.4b

Weights on Hugging Face: LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct.

Best for: chat, edge devices, phone, multilingual.

Can I Run EXAONE 3.5 2.4B on My GPU?

Other EXAONE 3.5 Sizes

EXAONE 3.5 2.4B — Frequently Asked Questions

How much VRAM does EXAONE 3.5 2.4B need?
About 2 GB at Q4_K_M — quantized weights plus framework overhead, before any KV cache. The cache grows with context length and is added on top; the table above folds it in. Apple Silicon counts unified memory toward the same figure.
Does EXAONE 3.5 2.4B run on an RTX 4090 (24 GB)?
Yes. EXAONE 3.5 2.4B needs about 2 GB at Q4_K_M, inside a 24 GB card, at an estimated 218 tokens/sec.
How do I run EXAONE 3.5 2.4B locally?
Install Ollama and run `ollama run exaone3.5:2.4b`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.
What other sizes does EXAONE 3.5 come in?
EXAONE 3.5 2.4B (2 GB), EXAONE 3.5 7.8B (6 GB), EXAONE 3.5 32B (20 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.

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