EXAONE-3.5-7.8B-Instruct
LGAI-EXAONEothertext

EXAONE-3.5-7.8B-Instruct

Updated Nov 5, 2026
Parameters
7.8B
Context
32,768
License
other
Q4 GGUF
4.77 GB
Updated
Nov 5, 2026

Q4 GGUF download: LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct-GGUF - 4.77 GB on disk, read 2026-11-05. VRAM figures above are estimates; this is the actual file size.

Will it run on your hardware?

Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest

Runs on your 24 GB - best at FP16
4 of 4 quantizations fit EXAONE-3.5-7.8B-Instruct with real runtime overhead.
Q4_K_M
5 GB
Q5_K_M
6 GB
Q8_0
8 GB
FP16
16 GB
fits tight too big

Need an exact figure for your context length? Use the VRAM calculator.

Run it locally

Copy-paste - running in under a minute

vLLMOpenAI-compatible API
vllm serve LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct

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Deep dive

Notes, sources, and the full write-up

EXAONE-3.5-7.8B-Instruct is a 7.8B-parameter other model from LGAI-EXAONE. At Q4_K_M it needs roughly 5 GB of VRAM, placing it in the 8–12 GB GPU hardware tier.

PopularityLink to this section

EXAONE-3.5-7.8B-Instruct has 376,826 downloads in the last month on HuggingFace and 160 likes.

Frequently asked

Quick answers to common questions

How much VRAM does EXAONE-3.5-7.8B-Instruct need?

EXAONE-3.5-7.8B-Instruct with 7.8B parameters needs approximately 5 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.

Is EXAONE-3.5-7.8B-Instruct better than other LGAI-EXAONE models?

EXAONE-3.5-7.8B-Instruct has 7.8B parameters with 32,768 context - a strong choice for general use.

What license is EXAONE-3.5-7.8B-Instruct under?

EXAONE-3.5-7.8B-Instruct is released under the other license, making it suitable for most commercial and personal projects.

What hardware runs EXAONE-3.5-7.8B-Instruct well?

With 7.8B parameters, EXAONE-3.5-7.8B-Instruct requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.

What is the best quantization for EXAONE-3.5-7.8B-Instruct?

Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~6 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.

How long can EXAONE-3.5-7.8B-Instruct's context window handle?

EXAONE-3.5-7.8B-Instruct supports a 32,768-token context window - enough for most medium-length documents and conversations. Real-world usable context may vary by implementation.

What models compete with EXAONE-3.5-7.8B-Instruct?

EXAONE-3.5-7.8B-Instruct competes with other models in its class. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.

Nearby options

Similar models and compatible hardware by spec