Can I Run GLM-5 / GLM-5.1 on Beelink SER9 (Ryzen AI 9, 32 GB)?

Written by Jakub Rusinowski · Last updated May 1, 2026

Yes — comfortably

Yes, comfortably — GLM-5 32B at Q3_K_M needs about 16.3 GB of the 32 GB usable on Beelink SER9 (Ryzen AI 9, 32 GB), leaving ~15.7 GB spare and running at ~6.1 tok/s (estimated), with room for about 65,536 tokens of context.

Confidence: medium · Recommended quantization: Q3_K_M · Estimated speed: ~6.1 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)

GLM-5 / GLM-5.1 on Beelink SER9 (Ryzen AI 9, 32 GB): memory by quantization

Quant	Memory needed	Fits 32 GB?	Max context	Est. speed	Download
F16	66.6 GB	✗ No	—	—	64 GB
Q8_0	36.6 GB	✗ No	—	—	34 GB
Q6_K	28.9 GB	✓ Yes	16K	~3.3 tok/s	26.2 GB
Q5_K_M	25.3 GB	✓ Yes	32K	~3.8 tok/s	22.7 GB
Q4_K_M	22 GB	✓ Yes	32K	~4.4 tok/s	19.3 GB
Q3_K_M	16.3 GB	✓ Yes	64K	~6.1 tok/s	13.6 GB
Q2_K	13.2 GB	✓ Yes	64K	~7.7 tok/s	10.5 GB

Which GLM-5 / GLM-5.1 sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
GLM-5 744B	455.2 GB	✗ Too large	—
GLM-5.1 72B	46.7 GB	✗ Too large	—
GLM-5 32B	22 GB	✓ Fits	~4.4 tok/s
GLM-5 9B	7.4 GB	✓ Fits	~14.4 tok/s

What to watch out for

Q3_K_M is the only quantization that fits, and it is a heavily degraded one — expect noticeably worse output than the same model at Q4_K_M. A smaller model at Q4 is usually the better trade.
2 larger variants of GLM-5 / GLM-5.1 do not fit and would need CPU offload or different hardware.
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