Written by Jakub Rusinowski · Last updated March 15, 2026
Yes
Yes — Nemotron Cascade 2 30B at Q6_K needs about 27.2 GB of the 32 GB usable on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM) (~4.8 GB spare), at ~47.1 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: medium · Recommended quantization: Q6_K · Estimated speed: ~47.1 tok/s
| Usable memory for models | 32 GB |
| Memory bandwidth | 1792 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
| Quant | Memory needed | Fits 32 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 62.6 GB | ✗ No | — | — | 60 GB |
| Q8_0 | 34.5 GB | ✗ No | — | — | 31.9 GB |
| Q6_K | 27.2 GB | ✓ Yes | 16K | ~47.1 tok/s | 24.6 GB |
| Q5_K_M | 23.8 GB | ✓ Yes | 32K | ~53.4 tok/s | 21.3 GB |
| Q4_K_M | 20.7 GB | ✓ Yes | 32K | ~61 tok/s | 18.1 GB |
| Q3_K_M | 15.4 GB | ✓ Yes | 64K | ~80.5 tok/s | 12.8 GB |
| Q2_K | 12.4 GB | ✓ Yes | 64K | ~97.6 tok/s | 9.9 GB |
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Nemotron Cascade 2 70B | 45.4 GB | ✗ Too large | — |
| Nemotron Cascade 2 30B | 20.7 GB | ✓ Fits | ~61 tok/s |
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Yes — Nemotron Cascade 2 30B at Q6_K needs about 27.2 GB of the 32 GB usable on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM) (~4.8 GB spare), at ~47.1 tok/s (estimated), with room for about 16,384 tokens of context.
Q6_K — it needs about 27.2 GB of the 32 GB available, downloads as roughly 24.6 GB, and runs at an estimated 47.1 tokens/sec with up to 16K of context.
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
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