FitMyLLM
LG AI/Dense

LEXAONE-4.0-32B

🎉 License Updated! We are pleased to announce our more flexible licensing terms 🤗

chatThinkingTool Use
32B
Parameters
128K
Context length
19
Benchmarks
14
Quantizations
21K
HF downloads
Architecture
Dense
Released
2024-08-07
Layers
64
KV Heads
8
Head Dim
128
Family
exaone

Quantization Options

Context length:
QuantBitsVRAM @ 16KQuality
IQ3_XXS3.25
15.0 GB
13.5 + 1.5 KV
low
IQ3_XS3.5
16.0 GB
14.5 + 1.5 KV
low
Q3_K_S3.64
16.5 GB
15.0 + 1.5 KV
low
IQ3_M3.76
17.0 GB
15.5 + 1.5 KV
low
Q3_K_M4
18.0 GB
16.5 + 1.5 KV
low
Q3_K_L4.3
19.2 GB
17.7 + 1.5 KV
moderate
IQ4_XS4.46
19.8 GB
18.3 + 1.5 KV
moderate
Q4_K_S4.67
20.7 GB
19.2 + 1.5 KV
moderate
Q4_K_M4.89
21.5 GB
20.0 + 1.5 KV
good
Q5_K_S5.57
24.3 GB
22.8 + 1.5 KV
good
Q5_K_M5.7
24.8 GB
23.3 + 1.5 KV
good
Q6_K6.56
28.2 GB
26.7 + 1.5 KV
excellent
Q8_08.5
36.0 GB
34.5 + 1.5 KV
lossless
FP1616
66.0 GB
64.5 + 1.5 KV
lossless

Select your GPU above to see speed estimates and compatibility for each quantization.

Deploying for a team or in production? Size GPUs, cost & scaling in Enterprise →
READY TO RUN THIS?RENT BY THE HOUR

RENT A GPU AND RUN EXAONE-4.0-32B NOW

Spin up an A100 / H100 / 4090 in ~60s. Pay by the second. Cancel anytime.

Community Ratings

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Benchmarks (19)

MATH-50097.7
IFEval83.9
AIME80.0
AA Math80.0
LiveCodeBench74.7
GPQA Diamond73.9
MATH51.3
MMLU-PRO40.4
BBH39.8
IFBench36.3
SciCode34.4
τ²-Bench17.3
AA Intelligence16.7
AA Coding14.0
AA Long Context14.0
HLE10.5
MUSR5.2
GPQA5.0
Terminal-Bench3.8

Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run exaone:32b-q4_K_M

Tag may need adjustment — check ollama.com/library/exaone for available tags.

▸ SETUP GUIDE
>_

Auto-setup with fitmyllm CLI

Detects your GPU, recommends the best model, downloads it, and starts chatting — zero config. Benchmarks your speed and contributes anonymous data to improve predictions.

pip install fitmyllmthen run fitmyllmLearn more
Auto-detect GPULive tok/s in chatSpeed benchmarks9 inference engines

GPUs that can run this model

At Q4_K_M quantization. Sorted by minimum VRAM.

Apple M4 Pro (24GB)
24 GB VRAM • 273 GB/s
APPLE
$1399
NVIDIA L4 24GB
24 GB VRAM • 300 GB/s
NVIDIA
$2500
Apple M2 (24GB)
24 GB VRAM • 100 GB/s
APPLE
$999
Apple M3 (24GB)
24 GB VRAM • 100 GB/s
APPLE
$999
Apple M4 (24GB)
24 GB VRAM • 120 GB/s
APPLE
$699
NVIDIA Tesla M40 24 GB
24 GB VRAM • 288 GB/s
NVIDIA
NVIDIA Tesla P10
24 GB VRAM • 694 GB/s
NVIDIA
NVIDIA Tesla P40
24 GB VRAM • 347 GB/s
NVIDIA
NVIDIA RTX A5000
24 GB VRAM • 768 GB/s
NVIDIA
$2500
NVIDIA L40 CNX
24 GB VRAM • 864 GB/s
NVIDIA
$5000
NVIDIA L40G
24 GB VRAM • 864 GB/s
NVIDIA
$5000

Find the best GPU for EXAONE-4.0-32B

Build Hardware for EXAONE-4.0-32B

🎉 License Updated! We are pleased to announce our more flexible licensing terms 🤗

▸ SPEC SHEET

EXAONE-4.0-32B32B Dense.

▸ SPECIFICATIONS
PARAMETERS
32B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
128K tokens
CAPABILITIES
chat
RELEASE DATE
2024-08-07
PROVIDER
LG AI
FAMILY
exaone
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
IQ3_XXS3.2513.5 GB82%
IQ3_XS3.514.5 GB84%
Q3_K_S3.6415.0 GB85%
IQ3_M3.7615.5 GB86%
Q3_K_M416.5 GB88%
Q3_K_L4.317.7 GB90%
IQ4_XS4.4618.3 GB92%
Q4_K_S4.6719.2 GB93%
Q4_K_M4.8920.0 GB94%
Q5_K_S5.5722.8 GB96%
Q5_K_M5.723.3 GB96%
Q6_K6.5626.7 GB97%
Q8_08.534.5 GB100%
FP161664.5 GB100%
§ 01BENCHMARK SCORES
MMLU-PRO40.4
MATH51.3
IFEval83.9
BBH39.8
GPQA5.0
MUSR5.2
GPQA Diamond73.9
LiveCodeBench74.7
AIME80.0
MATH-50097.7
HLE10.5
AA Intelligence16.7
AA Coding14.0
AA Math80.0
aa_ifbench36.3
aa_terminal_bench3.8
aa_tau217.3
aa_scicode34.4
aa_lcr14.0
§ 02RUN COMMAND

Run EXAONE-4.0-32B locally with Ollama — needs 20.0 GB VRAM at Q4_K_M:

$ollama run exaone:32b
§ 03COMPATIBLE GPUs
30 @ Q4_K_M