Can I Run Nex-N2 on 32 GB system RAM?

Written by Jakub Rusinowski · Last updated September 8, 2026

Yes, but it is tight

Yes, but it is tight — Nex-N2 mini at Q4_K_M needs about 23.8 GB of the 25.6 GB usable on 32 GB system RAM, leaving only ~1.8 GB before the runtime starts swapping. Expect ~23.2 tok/s (estimated), with room for about 8,192 tokens of context.

Confidence: low · Recommended quantization: Q4_K_M · Estimated speed: ~23.2 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

Nex-N2 on 32 GB system RAM: memory by quantization

Quant	Memory needed	Fits 25.6 GB?	Max context	Est. speed	Download
F16	72.7 GB	✗ No	—	—	70 GB
Q8_0	39.9 GB	✗ No	—	—	37.2 GB
Q6_K	31.4 GB	✗ No	—	—	28.7 GB
Q5_K_M	27.5 GB	✗ No	—	—	24.8 GB
Q4_K_M	23.8 GB	✓ Yes	8K	~23.2 tok/s	21.1 GB
Q3_K_M	17.6 GB	✓ Yes	32K	~28.4 tok/s	14.9 GB
Q2_K	14.2 GB	✓ Yes	32K	~32.4 tok/s	11.5 GB

Which Nex-N2 sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Nex-N2 Pro	244.7 GB	✗ Too large	—
Nex-N2 mini	23.8 GB	✓ Fits	~23.2 tok/s

What to watch out for

1 larger variant of Nex-N2 does not fit and would need CPU offload or different hardware.
This model does not publish its full attention configuration, so the KV-cache share of these figures is inferred from its parameter count rather than computed exactly.
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 inferred from the parameter count because this model does not publish its full attention configuration.

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