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

Written by Jakub Rusinowski · Last updated August 15, 2026

Yes — comfortably

Yes, comfortably — GPT-OSS 20B at Q8_0 needs about 22.5 GB of the 32 GB usable on Beelink SER9 (Ryzen AI 9, 32 GB), leaving ~9.5 GB spare and running at ~8.8 tok/s (estimated), with room for about 131,072 tokens of context.

Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~8.8 tok/s

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

Usable memory for models32 GB
Memory bandwidth256 GB/s
Form factorMini PC
Operating systemWindows or Linux
Memory upgradeableYes
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

QuantMemory neededFits 32 GB?Max contextEst. speedDownload
F1641.2 GB✗ No40 GB
Q8_022.5 GB✓ Yes128K~8.8 tok/s21.3 GB
Q6_K17.6 GB✓ Yes128K~11.3 tok/s16.4 GB
Q5_K_M15.4 GB✓ Yes128K~13 tok/s14.2 GB
Q4_K_M13.3 GB✓ Yes128K~15.1 tok/s12.1 GB
Q3_K_M9.7 GB✓ Yes128K~21 tok/s8.5 GB
Q2_K7.8 GB✓ Yes128K~26.6 tok/s6.6 GB

Which GPT-OSS sizes fit

VariantNeeds at Q4_K_MFits?Est. speed
GPT-oss 120B73.9 GB✗ Too large
GPT-OSS 20B13.3 GB✓ Fits~15.1 tok/s

What to watch out for

Beelink SER9 32 GB limitations

Recommended setup

Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving

How these numbers are calculated

FAQ

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

Yes, comfortably — GPT-OSS 20B at Q8_0 needs about 22.5 GB of the 32 GB usable on Beelink SER9 (Ryzen AI 9, 32 GB), leaving ~9.5 GB spare and running at ~8.8 tok/s (estimated), with room for about 131,072 tokens of context.

Which quantization of GPT-OSS should I use on Beelink SER9 (Ryzen AI 9, 32 GB)?

Q8_0 — it needs about 22.5 GB of the 32 GB available, downloads as roughly 21.3 GB, and runs at an estimated 8.8 tokens/sec with up to 128K of context.

What limits GPT-OSS on Beelink SER9 (Ryzen AI 9, 32 GB)?

Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.

Which runtime should I use?

Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving

Other Computers

Other Models on Beelink SER9 (Ryzen AI 9, 32 GB)

GPT-OSS on GPUs

What This Model Is Good At

Model & Tools

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