Can I Run Nex-N2 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?
Autor: Jakub Rusinowski · Ostatnia aktualizacja: 8 września 2026
Yes
Yes — Nex-N2 mini at Q2_K needs about 14.2 GB of the 16 GB usable on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM) (~1.8 GB spare), at ~203.2 tok/s (estimated), with room for about 8,192 tokens of context.
Confidence: medium · Recommended quantization: Q2_K · Estimated speed: ~203.2 tok/s
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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 |
Nex-N2 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 | 72.7 GB | ✗ No | — | — | 70 GB |
| Q8_0 | 39.9 GB | ✗ No | — | — | 37.2 GB |
| Q6_K | 31.4 GB | ✗ No | — | — | 28.7 GB |
| Q5_K_M | 27.5 GB | ✗ No | — | — | 24.8 GB |
| Q4_K_M | 23.8 GB | ✗ No | — | — | 21.1 GB |
| Q3_K_M | 17.6 GB | ✗ No | — | — | 14.9 GB |
| Q2_K | 14.2 GB | ✓ Yes | 8K | ~203.2 tok/s | 11.5 GB |
Which Nex-N2 sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Nex-N2 Pro | 244.7 GB | ✗ Too large | — |
| Nex-N2 mini | 23.8 GB | ✗ Too large | — |
What to watch out for
- Q2_K 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 Nex-N2 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 Nex-N2 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?
Yes — Nex-N2 mini at Q2_K needs about 14.2 GB of the 16 GB usable on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM) (~1.8 GB spare), at ~203.2 tok/s (estimated), with room for about 8,192 tokens of context.
Which quantization of Nex-N2 should I use on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?
Q2_K — it needs about 14.2 GB of the 16 GB available, downloads as roughly 11.5 GB, and runs at an estimated 203.2 tokens/sec with up to 8K of context.
What limits Nex-N2 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)
- Nex-N2.5 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
- North Mini Code on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
- OLMo 2 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
- Phi 3.5 Family on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
- Phi-4 Family on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
Nex-N2 on GPUs
- Nex-N2 on NVIDIA GeForce RTX 5090
- Nex-N2 on NVIDIA GeForce RTX 5080
- Nex-N2 on NVIDIA GeForce RTX 5070 Ti
- Nex-N2 on NVIDIA GeForce RTX 5070