Can I Run Qwen 2.5 Family on Beelink SER9 (Ryzen AI 9, 32 GB)?

Superseded model. Qwen 2.5 Family has been superseded by Qwen 3. This page is kept for reference; the newer family is a better starting point. View Qwen 3 →

Written by Jakub Rusinowski · Last updated November 12, 2024

Yes, but it is tight

Yes, but it is tight — Qwen 2.5 Coder 32B at Q6_K needs about 29.2 GB of the 32 GB usable on Beelink SER9 (Ryzen AI 9, 32 GB), leaving only ~2.8 GB before the runtime starts swapping. Expect ~6.9 tok/s (estimated), with room for about 16,384 tokens of context.

Confidence: medium · Recommended quantization: Q6_K · Estimated speed: ~6.9 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)

Qwen 2.5 Family on Beelink SER9 (Ryzen AI 9, 32 GB): memory by quantization

Quant	Memory needed	Fits 32 GB?	Max context	Est. speed	Download
F16	66.9 GB	✗ No	—	—	64 GB
Q8_0	36.9 GB	✗ No	—	—	34 GB
Q6_K	29.2 GB	✓ Yes	16K	~6.9 tok/s	26.2 GB
Q5_K_M	25.6 GB	✓ Yes	16K	~7.9 tok/s	22.7 GB
Q4_K_M	22.3 GB	✓ Yes	32K	~9.2 tok/s	19.3 GB
Q3_K_M	16.6 GB	✓ Yes	64K	~12.7 tok/s	13.6 GB
Q2_K	13.5 GB	✓ Yes	64K	~16 tok/s	10.5 GB

Which Qwen 2.5 Family sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Qwen 2.5 72B Instruct	47 GB	✗ Too large	—
Qwen 2.5 Coder 32B	22.3 GB	✓ Fits	~9.2 tok/s
Qwen 2.5 14B Instruct	10.9 GB	✓ Fits	~19.8 tok/s
Qwen 2.5 7B Instruct	5.9 GB	✓ Fits	~36.5 tok/s

What to watch out for

1 larger variant of Qwen 2.5 Family 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 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.

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