Can I Run Nemotron Cascade 2 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?
Written by Jakub Rusinowski · Last updated March 15, 2026
Yes, but it is tight
Yes, but it is tight — Nemotron Cascade 2 30B at Q3_K_M needs about 15.4 GB of the 16 GB usable on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM), leaving only ~0.6 GB before the runtime starts swapping. Expect ~47.1 tok/s (estimated), with room for about 8,192 tokens of context.
Confidence: low · Recommended quantization: Q3_K_M · Estimated speed: ~47.1 tok/s
RTX 5080 Desktop (16 GB VRAM, 32 GB RAM) — what it gives a model
| Usable memory for models | 16 GB |
| Memory bandwidth | 960 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
Nemotron Cascade 2 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM): memory by quantization
| Quant | Memory needed | Fits 16 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 | ✗ No | — | — | 24.6 GB |
| Q5_K_M | 23.8 GB | ✗ No | — | — | 21.3 GB |
| Q4_K_M | 20.7 GB | ✗ No | — | — | 18.1 GB |
| Q3_K_M | 15.4 GB | ✓ Yes | 8K | ~47.1 tok/s | 12.8 GB |
| Q2_K | 12.4 GB | ✓ Yes | 16K | ~58.2 tok/s | 9.9 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 | 20.7 GB | ✗ Too large | — |
What to watch out for
- Only ~0.6 GB of headroom at Q3_K_M: a longer context or a second application can push this into swapping.
- Q3_K_M is the only quantization that fits, and it is a heavily degraded one — expect noticeably worse output than the same model at Q4_K_M. A smaller model at Q4 is usually the better trade.
- 2 larger variants of Nemotron Cascade 2 do 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 5080 desktop limitations
- 16 GB VRAM is the binding constraint, not compute — a slower 24 GB card runs strictly more models.
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.
- 16 GB of VRAM on the NVIDIA GeForce RTX 5080 at 960 GB/s.
- 32 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 5080 Desktop (16 GB VRAM, 32 GB RAM)?
Yes, but it is tight — Nemotron Cascade 2 30B at Q3_K_M needs about 15.4 GB of the 16 GB usable on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM), leaving only ~0.6 GB before the runtime starts swapping. Expect ~47.1 tok/s (estimated), with room for about 8,192 tokens of context.
Which quantization of Nemotron Cascade 2 should I use on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?
Q3_K_M — it needs about 15.4 GB of the 16 GB available, downloads as roughly 12.8 GB, and runs at an estimated 47.1 tokens/sec with up to 8K of context.
What limits Nemotron Cascade 2 on RTX 5080 Desktop (16 GB VRAM, 32 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 5080 Desktop (16 GB VRAM, 32 GB RAM)
Nemotron Cascade 2 on GPUs
What This Model Is Good At
Model & Tools
← Can I Run It? | Nemotron Cascade 2 model page | Check your hardware