Can I Run GPT-OSS on Beelink SER9 (Ryzen AI 9, 32 GB)?

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 15 sierpnia 2026

Yes — comfortably

Yes, comfortably — GPT-OSS 20B at Q8_0 needs about 23.4 GB of the 32 GB usable on Beelink SER9 (Ryzen AI 9, 32 GB), leaving ~8.6 GB spare and running at ~43.1 tok/s (estimated), with room for about 131,072 tokens of context.

Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~43.1 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	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)

GPT-OSS on Beelink SER9 (Ryzen AI 9, 32 GB): memory by quantization

Quant	Memory needed	Fits 32 GB?	Max context	Est. speed	Download
F16	43 GB	✗ No	—	—	41.8 GB
Q8_0	23.4 GB	✓ Yes	128K	~43.1 tok/s	22.2 GB
Q6_K	18.3 GB	✓ Yes	128K	~53.6 tok/s	17.1 GB
Q5_K_M	16 GB	✓ Yes	128K	~60.3 tok/s	14.8 GB
Q4_K_M	13.8 GB	✓ Yes	128K	~68.4 tok/s	12.6 GB
Q3_K_M	10.1 GB	✓ Yes	128K	~88.6 tok/s	8.9 GB
Q2_K	8.1 GB	✓ Yes	128K	~105.7 tok/s	6.9 GB

Which GPT-OSS sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
GPT-oss 120B	71.9 GB	✗ Too large	—
GPT-OSS 20B	13.8 GB	✓ Fits	~68.4 tok/s

What to watch out for

1 larger variant of GPT-OSS 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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