Can I Run Nemotron 3 Nano Omni on Beelink SER9 (Ryzen AI 9, 32 GB)?
Written by Jakub Rusinowski · Last updated September 6, 2026
Yes
Yes — Nemotron 3 Nano Omni 30B-A3B at Q6_K needs about 27.2 GB of the 32 GB usable on Beelink SER9 (Ryzen AI 9, 32 GB) (~4.8 GB spare), at ~25.3 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: medium · Recommended quantization: Q6_K · Estimated speed: ~25.3 tok/s
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Beelink SER9 (Ryzen AI 9, 32 GB) — what it gives a model
| Usable memory for models | 32 GB |
| Memory bandwidth | 120 GB/s |
| Form factor | Mini PC |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
| Price | $859 (lib/data/ai-stations.ts (street price), checked 2026-07-06) |
Nemotron 3 Nano Omni on Beelink SER9 (Ryzen AI 9, 32 GB): memory by quantization
| 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 | ~25.3 tok/s | 24.6 GB |
| Q5_K_M | 23.8 GB | ✓ Yes | 32K | ~28 tok/s | 21.3 GB |
| Q4_K_M | 20.7 GB | ✓ Yes | 32K | ~31 tok/s | 18.1 GB |
| Q3_K_M | 15.4 GB | ✓ Yes | 64K | ~38 tok/s | 12.8 GB |
| Q2_K | 12.4 GB | ✓ Yes | 64K | ~43.3 tok/s | 9.9 GB |
What to watch out for
- 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.
Beelink SER9 32 GB limitations
- Shares system memory with the iGPU, so the usable model budget is well below the nominal 32 GB.
- Memory bandwidth, not capacity, is the limit here — expect single-digit tokens/sec on large 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.
- 32 GB unified memory at 120 GB/s, shared between CPU and GPU.
- 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 3 Nano Omni on Beelink SER9 (Ryzen AI 9, 32 GB)?
Yes — Nemotron 3 Nano Omni 30B-A3B at Q6_K needs about 27.2 GB of the 32 GB usable on Beelink SER9 (Ryzen AI 9, 32 GB) (~4.8 GB spare), at ~25.3 tok/s (estimated), with room for about 16,384 tokens of context.
Which quantization of Nemotron 3 Nano Omni should I use on Beelink SER9 (Ryzen AI 9, 32 GB)?
Q6_K — it needs about 27.2 GB of the 32 GB available, downloads as roughly 24.6 GB, and runs at an estimated 25.3 tokens/sec with up to 16K of context.
What limits Nemotron 3 Nano Omni on Beelink SER9 (Ryzen AI 9, 32 GB)?
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 Models on Beelink SER9 (Ryzen AI 9, 32 GB)
- Nemotron 70B on Beelink SER9 (Ryzen AI 9, 32 GB)
- Nemotron Cascade 2 on Beelink SER9 (Ryzen AI 9, 32 GB)
- Nex-N2 on Beelink SER9 (Ryzen AI 9, 32 GB)
- Nex-N2.5 on Beelink SER9 (Ryzen AI 9, 32 GB)
- North Mini Code on Beelink SER9 (Ryzen AI 9, 32 GB)
Nemotron 3 Nano Omni on GPUs
- Nemotron 3 Nano Omni on NVIDIA GeForce RTX 5090
- Nemotron 3 Nano Omni on NVIDIA GeForce RTX 5080
- Nemotron 3 Nano Omni on NVIDIA GeForce RTX 5070 Ti
- Nemotron 3 Nano Omni on NVIDIA GeForce RTX 5070
What This Model Is Good At
Model & Tools
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