Can I Run IBM Granite 4.1 on Beelink SER9 (Ryzen AI 9, 32 GB)?

Superseded model. IBM Granite 4.1 has been superseded by IBM Granite 4.2. This page is kept for reference; the newer family is a better starting point. View IBM Granite 4.2 →

Written by Jakub Rusinowski · Last updated April 29, 2026

Yes — comfortably

Yes, comfortably — Granite 4.1 30B at Q3_K_M needs about 15.4 GB of the 32 GB usable on Beelink SER9 (Ryzen AI 9, 32 GB), leaving ~16.6 GB spare and running at ~6.5 tok/s (estimated), with room for about 65,536 tokens of context.

Confidence: medium · Recommended quantization: Q3_K_M · Estimated speed: ~6.5 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)

IBM Granite 4.1 on Beelink SER9 (Ryzen AI 9, 32 GB): memory by quantization

Quant	Memory needed	Fits 32 GB?	Max context	Est. speed	Download
F16	62.6 GB	✗ No	—	—	60 GB
Q8_0	34.5 GB	✗ No	—	—	31.9 GB
Q6_K	27.2 GB	✓ Yes	16K	~3.5 tok/s	24.6 GB
Q5_K_M	23.8 GB	✓ Yes	32K	~4 tok/s	21.3 GB
Q4_K_M	20.7 GB	✓ Yes	32K	~4.7 tok/s	18.1 GB
Q3_K_M	15.4 GB	✓ Yes	64K	~6.5 tok/s	12.8 GB
Q2_K	12.4 GB	✓ Yes	64K	~8.2 tok/s	9.9 GB

Which IBM Granite 4.1 sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Granite 4.1 30B	20.7 GB	✓ Fits	~4.7 tok/s
Granite 4.1 8B	6.8 GB	✓ Fits	~16.1 tok/s
Granite 4.1 3B	3.5 GB	✓ Fits	~37 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.
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