FitMyLLM
exaone/Dense

EEXAONE Deep 7.8B

We introduce EXAONE Deep, which exhibits superior capabilities in various reasoning tasks including math and coding benchmarks, ranging from 2.4B to 32B parameters developed and released by LG AI Research.

reasoningmathcoding
7.8B
Parameters
32K
Context length
7
Benchmarks
6
Quantizations
Architecture
Dense
Released
2025-03-16
Layers
32
KV Heads
8
Head Dim
128
Family
exaone

Quantization Options

Context length:
QuantBitsVRAM @ 16KQuality
Q4_K_M4.89
6.8 GB
5.3 + 1.5 KV
good
Q5_K_S5.57
7.4 GB
5.9 + 1.5 KV
good
Q5_K_M5.7
7.5 GB
6.0 + 1.5 KV
good
Q6_K6.56
8.4 GB
6.9 + 1.5 KV
excellent
Q8_08.5
10.3 GB
8.8 + 1.5 KV
lossless
FP1616
17.6 GB
16.1 + 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 DEEP 7.8B NOW

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

Community Ratings

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Benchmarks (7)

MATH-50094.8
IFEval81.4
MATH47.5
MMLU-PRO34.8
BBH25.7
MUSR4.9
GPQA1.0

Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run exaone-deep:7.8b-q4_K_M

Downloads and runs automatically. Add --verbose for speed stats.

▸ 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.

NVIDIA Tesla C2070
6 GB VRAM • 143 GB/s
NVIDIA
NVIDIA Tesla C2075
6 GB VRAM • 150 GB/s
NVIDIA
NVIDIA Tesla C2090
6 GB VRAM • 177 GB/s
NVIDIA
NVIDIA Tesla M2070
6 GB VRAM • 150 GB/s
NVIDIA
NVIDIA Tesla M2070-Q
6 GB VRAM • 150 GB/s
NVIDIA
NVIDIA Tesla M2075
6 GB VRAM • 150 GB/s
NVIDIA
NVIDIA Tesla M2090
6 GB VRAM • 177 GB/s
NVIDIA
NVIDIA Tesla X2070
6 GB VRAM • 177 GB/s
NVIDIA
NVIDIA Tesla X2090
6 GB VRAM • 177 GB/s
NVIDIA
NVIDIA Tesla K20X
6 GB VRAM • 250 GB/s
NVIDIA
NVIDIA Tesla K20Xm
6 GB VRAM • 250 GB/s
NVIDIA

Find the best GPU for EXAONE Deep 7.8B

Build Hardware for EXAONE Deep 7.8B
▸ SPEC SHEET

EXAONE Deep 7.8B7.8B Dense.

▸ SPECIFICATIONS
PARAMETERS
7.8B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
32K tokens
CAPABILITIES
reasoning, math, coding
RELEASE DATE
2025-03-16
FAMILY
exaone
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
Q4_K_M4.895.3 GB94%
Q5_K_S5.575.9 GB96%
Q5_K_M5.76.0 GB96%
Q6_K6.566.9 GB97%
Q8_08.58.8 GB100%
FP161616.1 GB100%
§ 01BENCHMARK SCORES
MMLU-PRO34.8
MATH47.5
IFEval81.4
BBH25.7
GPQA1.0
MUSR4.9
MATH-50094.8
§ 02RUN COMMAND

Run EXAONE Deep 7.8B locally with Ollama — needs 5.3 GB VRAM at Q4_K_M:

$ollama run exaone-deep:7.8b
§ 03COMPATIBLE GPUs
30 @ Q4_K_M