Can I Run Nemotron Cascade 2 on 32 GB system RAM?
作者： Jakub Rusinowski · 最后更新： 2026年9月11日
Yes, but it is tight
Yes, but it is tight — Nemotron-Cascade 2 30B-A3B at Q5_K_M needs about 25 GB of the 25.6 GB usable on 32 GB system RAM, leaving only ~0.6 GB before the runtime starts swapping. Expect ~19 tok/s (estimated), with room for about 8,192 tokens of context.
Confidence: low · Recommended quantization: Q5_K_M · Estimated speed: ~19 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 |
Nemotron Cascade 2 on 32 GB system RAM: memory by quantization
| Quant | Memory needed | Fits 25.6 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 65.8 GB | ✗ No | — | — | 63.2 GB |
| Q8_0 | 36.2 GB | ✗ No | — | — | 33.6 GB |
| Q6_K | 28.5 GB | ✗ No | — | — | 25.9 GB |
| Q5_K_M | 25 GB | ✓ Yes | 8K | ~19 tok/s | 22.4 GB |
| Q4_K_M | 21.7 GB | ✓ Yes | 16K | ~21.2 tok/s | 19.1 GB |
| Q3_K_M | 16.1 GB | ✓ Yes | 32K | ~26.4 tok/s | 13.5 GB |
| Q2_K | 13 GB | ✓ Yes | 32K | ~30.4 tok/s | 10.4 GB |
What to watch out for
- Only ~0.6 GB of headroom at Q5_K_M: a longer context or a second application can push this into swapping.
- 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.
FAQ
Can I run Nemotron Cascade 2 on 32 GB system RAM?
Yes, but it is tight — Nemotron-Cascade 2 30B-A3B at Q5_K_M needs about 25 GB of the 25.6 GB usable on 32 GB system RAM, leaving only ~0.6 GB before the runtime starts swapping. Expect ~19 tok/s (estimated), with room for about 8,192 tokens of context.
Which quantization of Nemotron Cascade 2 should I use on 32 GB system RAM?
Q5_K_M — it needs about 25 GB of the 25.6 GB available, downloads as roughly 22.4 GB, and runs at an estimated 19 tokens/sec with up to 8K of context.
What limits Nemotron Cascade 2 on 32 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 32 GB system RAM
- Nex-N2 on 32 GB system RAM
- Nex-N2.5 on 32 GB system RAM
- North Mini Code on 32 GB system RAM
- OLMo 2 on 32 GB system RAM
- Phi 3.5 Family on 32 GB system RAM
Nemotron Cascade 2 on GPUs
- Nemotron Cascade 2 on NVIDIA GeForce RTX 5090
- Nemotron Cascade 2 on NVIDIA GeForce RTX 5080
- Nemotron Cascade 2 on NVIDIA GeForce RTX 5070 Ti
- Nemotron Cascade 2 on NVIDIA GeForce RTX 5070
What This Model Is Good At
Model & Tools
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