Can I Run Nemotron Cascade 2 on 192 GB system RAM?

作者： Jakub Rusinowski · 最后更新： 2026年9月11日

Yes — comfortably

Yes, comfortably — Nemotron-Cascade 2 30B-A3B at Q8_0 needs about 36.2 GB of the 153.6 GB usable on 192 GB system RAM, leaving ~117.4 GB spare and running at ~14.1 tok/s (estimated), with room for about 262,144 tokens of context.

Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~14.1 tok/s

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192 GB system RAM — what it gives a model

Usable memory for models	153.6 GB
Memory bandwidth	90 GB/s

Nemotron Cascade 2 on 192 GB system RAM: memory by quantization

Quant	Memory needed	Fits 153.6 GB?	Max context	Est. speed	Download
F16	65.8 GB	✓ Yes	256K	~8.3 tok/s	63.2 GB
Q8_0	36.2 GB	✓ Yes	256K	~14.1 tok/s	33.6 GB
Q6_K	28.5 GB	✓ Yes	256K	~17.1 tok/s	25.9 GB
Q5_K_M	25 GB	✓ Yes	256K	~19 tok/s	22.4 GB
Q4_K_M	21.7 GB	✓ Yes	256K	~21.2 tok/s	19.1 GB
Q3_K_M	16.1 GB	✓ Yes	256K	~26.4 tok/s	13.5 GB
Q2_K	13 GB	✓ Yes	256K	~30.4 tok/s	10.4 GB

Which Nemotron Cascade 2 sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Nemotron Cascade 2 70B (Unverified Listing)	45.4 GB	✓ Fits	~1.5 tok/s
Nemotron-Cascade 2 30B-A3B	21.7 GB	✓ Fits	~21.2 tok/s

What to watch out for

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.
153.6 GB of the 192 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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