Can I Run Nemotron Cascade 2 on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)?
Written by Jakub Rusinowski · Last updated September 11, 2026
Yes, but it is tight
Yes, but it is tight — Nemotron-Cascade 2 30B-A3B at Q4_K_M needs about 21.7 GB of the 24 GB usable on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM), leaving only ~2.3 GB before the runtime starts swapping. Expect ~152.7 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: low · Recommended quantization: Q4_K_M · Estimated speed: ~152.7 tok/s
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RTX 3090 Desktop (24 GB VRAM, 64 GB RAM) — what it gives a model
| Usable memory for models | 24 GB |
| Memory bandwidth | 936 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
Nemotron Cascade 2 on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM): memory by quantization
| Quant | Memory needed | Fits 24 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 | ✗ No | — | — | 22.4 GB |
| Q4_K_M | 21.7 GB | ✓ Yes | 16K | ~152.7 tok/s | 19.1 GB |
| Q3_K_M | 16.1 GB | ✓ Yes | 32K | ~176.6 tok/s | 13.5 GB |
| Q2_K | 13 GB | ✓ Yes | 32K | ~193.2 tok/s | 10.4 GB |
Which Nemotron Cascade 2 sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Nemotron Cascade 2 70B | 45.4 GB | ✗ Too large | — |
| Nemotron-Cascade 2 30B-A3B | 21.7 GB | ✓ Fits | ~152.7 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 3090 desktop limitations
- The cheapest route to 24 GB of VRAM, and the standard used-market recommendation for local LLMs.
- Older architecture: no FP8 acceleration, and higher idle power than a current card.
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.
- 24 GB of VRAM on the NVIDIA GeForce RTX 3090 at 936 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.
FAQ
Can I run Nemotron Cascade 2 on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)?
Yes, but it is tight — Nemotron-Cascade 2 30B-A3B at Q4_K_M needs about 21.7 GB of the 24 GB usable on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM), leaving only ~2.3 GB before the runtime starts swapping. Expect ~152.7 tok/s (estimated), with room for about 16,384 tokens of context.
Which quantization of Nemotron Cascade 2 should I use on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)?
Q4_K_M — it needs about 21.7 GB of the 24 GB available, downloads as roughly 19.1 GB, and runs at an estimated 152.7 tokens/sec with up to 16K of context.
What limits Nemotron Cascade 2 on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)?
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Which runtime should I use?
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Other Computers
Other Models on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)
- Nex-N2 on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)
- Nex-N2.5 on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)
- North Mini Code on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)
- OLMo 2 on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM)
- Phi 3.5 Family on RTX 3090 Desktop (24 GB VRAM, 64 GB 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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