Can I Run Mistral Small 3.1 on Beelink SER9 (Ryzen AI 9, 32 GB)?
Written by Jakub Rusinowski · Last updated March 17, 2025
Yes — comfortably
Yes, comfortably — Mistral Small 3.1 24B at Q5_K_M needs about 18.9 GB of the 32 GB usable on Beelink SER9 (Ryzen AI 9, 32 GB), leaving ~13.1 GB spare and running at ~5.1 tok/s (estimated), with room for about 65,536 tokens of context.
Confidence: high · Recommended quantization: Q5_K_M · Estimated speed: ~5.1 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) |
Mistral Small 3.1 on Beelink SER9 (Ryzen AI 9, 32 GB): memory by quantization
| Quant | Memory needed | Fits 32 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 49.3 GB | ✗ No | — | — | 47.2 GB |
| Q8_0 | 27.2 GB | ✓ Yes | 32K | ~3.5 tok/s | 25.1 GB |
| Q6_K | 21.5 GB | ✓ Yes | 64K | ~4.4 tok/s | 19.4 GB |
| Q5_K_M | 18.9 GB | ✓ Yes | 64K | ~5.1 tok/s | 16.7 GB |
| Q4_K_M | 16.4 GB | ✓ Yes | 64K | ~6 tok/s | 14.2 GB |
| Q3_K_M | 12.2 GB | ✓ Yes | 64K | ~8.2 tok/s | 10.1 GB |
| Q2_K | 9.9 GB | ✓ Yes | 64K | ~10.4 tok/s | 7.8 GB |
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 Mistral Small 3.1 on Beelink SER9 (Ryzen AI 9, 32 GB)?
Yes, comfortably — Mistral Small 3.1 24B at Q5_K_M needs about 18.9 GB of the 32 GB usable on Beelink SER9 (Ryzen AI 9, 32 GB), leaving ~13.1 GB spare and running at ~5.1 tok/s (estimated), with room for about 65,536 tokens of context.
Which quantization of Mistral Small 3.1 should I use on Beelink SER9 (Ryzen AI 9, 32 GB)?
Q5_K_M — it needs about 18.9 GB of the 32 GB available, downloads as roughly 16.7 GB, and runs at an estimated 5.1 tokens/sec with up to 64K of context.
What limits Mistral Small 3.1 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 Computers
Other Models on Beelink SER9 (Ryzen AI 9, 32 GB)
- Mistral Small 3.2 on Beelink SER9 (Ryzen AI 9, 32 GB)
- Nemotron 3 Nano Omni 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)
Mistral Small 3.1 on GPUs
- Mistral Small 3.1 on NVIDIA GeForce RTX 5090
- Mistral Small 3.1 on NVIDIA GeForce RTX 5080
- Mistral Small 3.1 on NVIDIA GeForce RTX 5070 Ti
- Mistral Small 3.1 on NVIDIA GeForce RTX 5070
What This Model Is Good At
Model & Tools
← Can I Run It? | Mistral Small 3.1 model page | Check your hardware