Can I Run Nemotron 70B on Beelink SER9 (Ryzen AI 9, 32 GB)?
Autor: Jakub Rusinowski · Ostatnia aktualizacja: 15 października 2024
Technically yes, but not recommended
It loads, but it is not worth running — Nemotron 70B Instruct at Q2_K fits in Beelink SER9 (Ryzen AI 9, 32 GB)'s 32 GB, yet the memory bandwidth limits it to ~3.6 tok/s (estimated), well below usable interactive speed.
Confidence: high · Recommended quantization: Q2_K · Estimated speed: ~3.6 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 70B on Beelink SER9 (Ryzen AI 9, 32 GB): memory by quantization
| Quant | Memory needed | Fits 32 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 144.7 GB | ✗ No | — | — | 141.2 GB |
| Q8_0 | 78.5 GB | ✗ No | — | — | 75 GB |
| Q6_K | 61.4 GB | ✗ No | — | — | 57.9 GB |
| Q5_K_M | 53.5 GB | ✗ No | — | — | 50 GB |
| Q4_K_M | 46.1 GB | ✗ No | — | — | 42.6 GB |
| Q3_K_M | 33.6 GB | ✗ No | — | — | 30.1 GB |
| Q2_K | 26.7 GB | ✓ Yes | 16K | ~3.6 tok/s | 23.2 GB |
What to watch out for
- At ~3.6 tok/s this loads but is too slow for interactive use — expect roughly 17 seconds per 60 tokens.
- 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.
- 1 larger variant of Nemotron 70B does not fit and would need CPU offload or different hardware.
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 computed from this model's published attention configuration.
FAQ
Can I run Nemotron 70B on Beelink SER9 (Ryzen AI 9, 32 GB)?
It loads, but it is not worth running — Nemotron 70B Instruct at Q2_K fits in Beelink SER9 (Ryzen AI 9, 32 GB)'s 32 GB, yet the memory bandwidth limits it to ~3.6 tok/s (estimated), well below usable interactive speed.
Which quantization of Nemotron 70B should I use on Beelink SER9 (Ryzen AI 9, 32 GB)?
Q2_K — it needs about 26.7 GB of the 32 GB available, downloads as roughly 23.2 GB, and runs at an estimated 3.6 tokens/sec with up to 16K of context.
What limits Nemotron 70B on Beelink SER9 (Ryzen AI 9, 32 GB)?
Memory bandwidth. The model fits, but at 120 GB/s it can only be read fast enough for roughly 3.6 tokens/sec.
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 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)
- OLMo 2 on Beelink SER9 (Ryzen AI 9, 32 GB)
Nemotron 70B on GPUs
- Nemotron 70B on NVIDIA GeForce RTX 5090
- Nemotron 70B on NVIDIA GeForce RTX 4090
- Nemotron 70B on NVIDIA GeForce RTX 3090
- Nemotron 70B on AMD Radeon RX 7900 XTX
What This Model Is Good At
Model & Tools
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