Can I Run Nemotron Cascade 2 on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)?

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 11 września 2026

Yes

Yes — Nemotron-Cascade 2 30B-A3B at Q6_K needs about 28.5 GB of the 32 GB usable on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM) (~3.5 GB spare), at ~197.8 tok/s (estimated), with room for about 16,384 tokens of context.

Confidence: medium · Recommended quantization: Q6_K · Estimated speed: ~197.8 tok/s

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RTX 5090 Desktop (32 GB VRAM, 64 GB RAM) — what it gives a model

Usable memory for models	32 GB
Memory bandwidth	1792 GB/s
Form factor	Desktop
Operating system	Windows or Linux
Memory upgradeable	Yes

Nemotron Cascade 2 on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM): memory by quantization

Quant	Memory needed	Fits 32 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	✓ Yes	16K	~197.8 tok/s	25.9 GB
Q5_K_M	25 GB	✓ Yes	32K	~209.8 tok/s	22.4 GB
Q4_K_M	21.7 GB	✓ Yes	32K	~222.6 tok/s	19.1 GB
Q3_K_M	16.1 GB	✓ Yes	64K	~248.1 tok/s	13.5 GB
Q2_K	13 GB	✓ Yes	64K	~264.8 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	✗ Too large	—
Nemotron-Cascade 2 30B-A3B	21.7 GB	✓ Fits	~222.6 tok/s

What to watch out for

1 larger variant of Nemotron Cascade 2 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.

RTX 5090 desktop limitations

575 W board power — budget for a 1000 W+ PSU and the heat it puts into the room.
Models larger than 32 GB must offload to system RAM, which costs roughly an order of magnitude in speed.

Recommended setup

Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving

How these numbers are calculated

Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
32 GB of VRAM on the NVIDIA GeForce RTX 5090 at 1792 GB/s.
64 GB of system RAM available for CPU offload when a model exceeds VRAM.
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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