Can I Run Magistral Small on Beelink SER9 (Ryzen AI 9, 32 GB)?
Written by Jakub Rusinowski · Last updated September 6, 2026
Yes — comfortably
Yes, comfortably — Magistral Small 24B at Q5_K_M needs about 19.5 GB of the 32 GB usable on Beelink SER9 (Ryzen AI 9, 32 GB), leaving ~12.5 GB spare and running at ~5 tok/s (estimated), with room for about 65,536 tokens of context.
Confidence: medium · Recommended quantization: Q5_K_M · Estimated speed: ~5 tok/s
See what else this hardware can run →
or compare on Vast.ai from $0.35/hr (typical low · varies)
As an Amazon Associate we earn from qualifying purchases. Cloud GPU links are referral links — we may earn a commission at no extra cost to you.
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) |
Magistral Small on Beelink SER9 (Ryzen AI 9, 32 GB): memory by quantization
| Quant | Memory needed | Fits 32 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 50.5 GB | ✗ No | — | — | 48 GB |
| Q8_0 | 28 GB | ✓ Yes | 16K | ~3.4 tok/s | 25.5 GB |
| Q6_K | 22.2 GB | ✓ Yes | 32K | ~4.3 tok/s | 19.7 GB |
| Q5_K_M | 19.5 GB | ✓ Yes | 64K | ~5 tok/s | 17 GB |
| Q4_K_M | 17 GB | ✓ Yes | 64K | ~5.8 tok/s | 14.5 GB |
| Q3_K_M | 12.7 GB | ✓ Yes | 64K | ~8 tok/s | 10.2 GB |
| Q2_K | 10.4 GB | ✓ Yes | 64K | ~10.1 tok/s | 7.9 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 Magistral Small on Beelink SER9 (Ryzen AI 9, 32 GB)?
Yes, comfortably — Magistral Small 24B at Q5_K_M needs about 19.5 GB of the 32 GB usable on Beelink SER9 (Ryzen AI 9, 32 GB), leaving ~12.5 GB spare and running at ~5 tok/s (estimated), with room for about 65,536 tokens of context.
Which quantization of Magistral Small should I use on Beelink SER9 (Ryzen AI 9, 32 GB)?
Q5_K_M — it needs about 19.5 GB of the 32 GB available, downloads as roughly 17 GB, and runs at an estimated 5 tokens/sec with up to 64K of context.
What limits Magistral Small 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)
- MiniCPM-V on Beelink SER9 (Ryzen AI 9, 32 GB)
- Ministral on Beelink SER9 (Ryzen AI 9, 32 GB)
- Ministral 3 on Beelink SER9 (Ryzen AI 9, 32 GB)
- Mistral Family on Beelink SER9 (Ryzen AI 9, 32 GB)
- Mistral Small 3.1 on Beelink SER9 (Ryzen AI 9, 32 GB)
Magistral Small on GPUs
- Magistral Small on NVIDIA GeForce RTX 5090
- Magistral Small on NVIDIA GeForce RTX 5080
- Magistral Small on NVIDIA GeForce RTX 5070 Ti
- Magistral Small on NVIDIA GeForce RTX 5070
What This Model Is Good At
Model & Tools
← Can I Run It? | Magistral Small model page | Check your hardware