Can I Run EXAONE 3.5 on 32 GB system RAM?

作者： Jakub Rusinowski · 最后更新： 2026年2月10日

Yes — comfortably

Yes, comfortably — EXAONE 3.5 32B at Q2_K needs about 13.5 GB of the 25.6 GB usable on 32 GB system RAM, leaving ~12.1 GB spare and running at ~5.7 tok/s (estimated), with room for about 32,768 tokens of context.

Confidence: high · Recommended quantization: Q2_K · Estimated speed: ~5.7 tok/s

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32 GB system RAM — what it gives a model

Usable memory for models	25.6 GB
Memory bandwidth	90 GB/s

EXAONE 3.5 on 32 GB system RAM: memory by quantization

Quant	Memory needed	Fits 25.6 GB?	Max context	Est. speed	Download
F16	66.9 GB	✗ No	—	—	64 GB
Q8_0	36.9 GB	✗ No	—	—	34 GB
Q6_K	29.2 GB	✗ No	—	—	26.2 GB
Q5_K_M	25.6 GB	✗ No	—	—	22.7 GB
Q4_K_M	22.3 GB	✓ Yes	16K	~3.3 tok/s	19.3 GB
Q3_K_M	16.6 GB	✓ Yes	32K	~4.5 tok/s	13.6 GB
Q2_K	13.5 GB	✓ Yes	32K	~5.7 tok/s	10.5 GB

Which EXAONE 3.5 sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
EXAONE 3.5 32B	22.3 GB	✓ Fits	~3.3 tok/s
EXAONE 3.5 7.8B	6.6 GB	✓ Fits	~12.5 tok/s
EXAONE 3.5 2.4B	2.9 GB	✓ Fits	~35.1 tok/s

What to watch out for

Q2_K is the only quantization that fits, and it is a heavily degraded one — expect noticeably worse output than the same model at Q4_K_M. A smaller model at Q4 is usually the better trade.
These figures assume CPU-only inference. Any discrete GPU, even an 8 GB one, will be several times faster for models that fit in its VRAM.

Recommended setup

llama.cpp (CPU build) or Ollama — both run without a GPU

How these numbers are calculated

Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
25.6 GB of the 32 GB is treated as usable for model weights (80% — the rest is the OS and running applications).
DDR5-5600 dual channel at 89.6 GB/s peak. CPU decode is assumed to sustain 35% of that peak, because CPU inference is not purely bandwidth-bound — it also spends real time in compute and thread synchronisation. This figure is an assumption, not a fitted constant: no CPU measurement is in the calibration set.
CPU-only inference: no GPU is assumed. A GPU of any size will beat these figures substantially.
Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
KV cache is computed from this model's published attention configuration.

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