Can I Run Ministral on Beelink SER9 (Ryzen AI 9, 32 GB)?

Superseded model. Ministral has been superseded by Mistral Small 4. This page is kept for reference; the newer family is a better starting point. View Mistral Small 4 →

Written by Jakub Rusinowski · Last updated October 16, 2024

Yes — comfortably

Yes, comfortably — Ministral 8B at Q8_0 needs about 10.5 GB of the 32 GB usable on Beelink SER9 (Ryzen AI 9, 32 GB), leaving ~21.5 GB spare and running at ~20.1 tok/s (estimated), with room for about 32,768 tokens of context.

Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~20.1 tok/s

Beelink SER9 (Ryzen AI 9, 32 GB) — what it gives a model

Usable memory for models	32 GB
Memory bandwidth	256 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)

Ministral on Beelink SER9 (Ryzen AI 9, 32 GB): memory by quantization

Quant	Memory needed	Fits 32 GB?	Max context	Est. speed	Download
F16	18 GB	✓ Yes	32K	~11.2 tok/s	16 GB
Q8_0	10.5 GB	✓ Yes	32K	~20.1 tok/s	8.5 GB
Q6_K	8.6 GB	✓ Yes	32K	~25.2 tok/s	6.6 GB
Q5_K_M	7.7 GB	✓ Yes	32K	~28.6 tok/s	5.7 GB
Q4_K_M	6.9 GB	✓ Yes	32K	~32.7 tok/s	4.8 GB
Q3_K_M	5.4 GB	✓ Yes	32K	~43.1 tok/s	3.4 GB
Q2_K	4.6 GB	✓ Yes	32K	~52.3 tok/s	2.6 GB

Which Ministral sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Ministral 8B	6.9 GB	✓ Fits	~32.7 tok/s
Ministral 3B	3.8 GB	✓ Fits	~63.1 tok/s

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 256 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