Can I Run Gemma 4 on Beelink SER9 (Ryzen AI 9, 32 GB)?

Written by Jakub Rusinowski · Last updated June 3, 2026

Yes

Yes — Gemma 4 31B at Q6_K needs about 28 GB of the 32 GB usable on Beelink SER9 (Ryzen AI 9, 32 GB) (~4 GB spare), at ~7.2 tok/s (estimated), with room for about 16,384 tokens of context.

Confidence: medium · Recommended quantization: Q6_K · Estimated speed: ~7.2 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)

Gemma 4 on Beelink SER9 (Ryzen AI 9, 32 GB): memory by quantization

QuantMemory neededFits 32 GB?Max contextEst. speedDownload
F1664.6 GB✗ No62 GB
Q8_035.5 GB✗ No32.9 GB
Q6_K28 GB✓ Yes16K~7.2 tok/s25.4 GB
Q5_K_M24.6 GB✓ Yes32K~8.2 tok/s22 GB
Q4_K_M21.3 GB✓ Yes32K~9.6 tok/s18.7 GB
Q3_K_M15.8 GB✓ Yes64K~13.2 tok/s13.2 GB
Q2_K12.8 GB✓ Yes64K~16.7 tok/s10.2 GB

Which Gemma 4 sizes fit

VariantNeeds at Q4_K_MFits?Est. speed
Gemma 4 31B21.3 GB✓ Fits~9.6 tok/s
Gemma 4 26B-A4B18.2 GB✓ Fits~51.9 tok/s
Gemma 4 12B (Unified)9.4 GB✓ Fits~23 tok/s
Gemma 4 E4B6.8 GB✓ Fits~32.9 tok/s
Gemma 4 E2B4.9 GB✓ Fits~47.8 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 Gemma 4 on Beelink SER9 (Ryzen AI 9, 32 GB)?

Yes — Gemma 4 31B at Q6_K needs about 28 GB of the 32 GB usable on Beelink SER9 (Ryzen AI 9, 32 GB) (~4 GB spare), at ~7.2 tok/s (estimated), with room for about 16,384 tokens of context.

Which quantization of Gemma 4 should I use on Beelink SER9 (Ryzen AI 9, 32 GB)?

Q6_K — it needs about 28 GB of the 32 GB available, downloads as roughly 25.4 GB, and runs at an estimated 7.2 tokens/sec with up to 16K of context.

What limits Gemma 4 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)

Gemma 4 on GPUs

What This Model Is Good At

Model & Tools

← Can I Run It? | Gemma 4 model page | Check your hardware