Can I Run Nex-N2.5 on 64 GB system RAM?
Written by Jakub Rusinowski · Last updated September 8, 2026
Yes
Yes — Nex-N2.5 mini at Q8_0 needs about 39.9 GB of the 51.2 GB usable on 64 GB system RAM (~11.3 GB spare), at ~15.7 tok/s (estimated), with room for about 32,768 tokens of context.
Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~15.7 tok/s
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64 GB system RAM — what it gives a model
| Usable memory for models | 51.2 GB |
| Memory bandwidth | 90 GB/s |
Nex-N2.5 on 64 GB system RAM: memory by quantization
| Quant | Memory needed | Fits 51.2 GB? | Max context | Est. speed | Download |
|---|
| F16 | 72.7 GB | ✗ No | — | — | 70 GB |
| Q8_0 | 39.9 GB | ✓ Yes | 32K | ~15.7 tok/s | 37.2 GB |
| Q6_K | 31.4 GB | ✓ Yes | 64K | ~18.9 tok/s | 28.7 GB |
| Q5_K_M | 27.5 GB | ✓ Yes | 64K | ~20.9 tok/s | 24.8 GB |
| Q4_K_M | 23.8 GB | ✓ Yes | 64K | ~23.2 tok/s | 21.1 GB |
| Q3_K_M | 17.6 GB | ✓ Yes | 128K | ~28.4 tok/s | 14.9 GB |
| Q2_K | 14.2 GB | ✓ Yes | 128K | ~32.4 tok/s | 11.5 GB |
Which Nex-N2.5 sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|
| Nex-N2.5 Max | 967.4 GB | ✗ Too large | — |
| Nex-N2.5 Pro | 244.7 GB | ✗ Too large | — |
| Nex-N2.5 mini | 23.8 GB | ✓ Fits | ~23.2 tok/s |
What to watch out for
- 2 larger variants of Nex-N2.5 do 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.
- 51.2 GB of the 64 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.
FAQ
Can I run Nex-N2.5 on 64 GB system RAM?
Yes — Nex-N2.5 mini at Q8_0 needs about 39.9 GB of the 51.2 GB usable on 64 GB system RAM (~11.3 GB spare), at ~15.7 tok/s (estimated), with room for about 32,768 tokens of context.
Which quantization of Nex-N2.5 should I use on 64 GB system RAM?
Q8_0 — it needs about 39.9 GB of the 51.2 GB available, downloads as roughly 37.2 GB, and runs at an estimated 15.7 tokens/sec with up to 32K of context.
What limits Nex-N2.5 on 64 GB system RAM?
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Which runtime should I use?
llama.cpp (CPU build) or Ollama — both run without a GPU
Other RAM Capacities
Other Models on 64 GB system RAM
Nex-N2.5 on GPUs
What This Model Is Good At
Model & Tools
← Can I Run It? | Nex-N2.5 model page | Check your hardware