Can I Run Gemma 3 on Beelink SER9 (Ryzen AI 9, 32 GB)?
Written by Jakub Rusinowski · Last updated March 12, 2025
Yes — comfortably
Yes, comfortably — Gemma 3 27B Instruct at Q3_K_M needs about 20.6 GB of the 32 GB usable on Beelink SER9 (Ryzen AI 9, 32 GB), leaving ~11.4 GB spare and running at ~5.7 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: high · Recommended quantization: Q3_K_M · Estimated speed: ~5.7 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) |
Gemma 3 on Beelink SER9 (Ryzen AI 9, 32 GB): memory by quantization
| Quant | Memory needed | Fits 32 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 63.1 GB | ✗ No | — | — | 54 GB |
| Q8_0 | 37.8 GB | ✗ No | — | — | 28.7 GB |
| Q6_K | 31.3 GB | ✓ Yes | 8K | ~3.4 tok/s | 22.1 GB |
| Q5_K_M | 28.3 GB | ✓ Yes | 8K | ~3.8 tok/s | 19.1 GB |
| Q4_K_M | 25.4 GB | ✓ Yes | 8K | ~4.4 tok/s | 16.3 GB |
| Q3_K_M | 20.6 GB | ✓ Yes | 16K | ~5.7 tok/s | 11.5 GB |
| Q2_K | 18 GB | ✓ Yes | 16K | ~6.8 tok/s | 8.9 GB |
Which Gemma 3 sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Gemma 3 27B Instruct | 25.4 GB | ✓ Fits | ~4.4 tok/s |
| Gemma 3 12B Instruct | 11.1 GB | ✓ Fits | ~10 tok/s |
| Gemma 3 4B Instruct | 4.4 GB | ✓ Fits | ~28.2 tok/s |
| Gemma 3 1B Instruct | 2 GB | ✓ Fits | ~81.2 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.
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 computed from this model's published attention configuration.
FAQ
Can I run Gemma 3 on Beelink SER9 (Ryzen AI 9, 32 GB)?
Yes, comfortably — Gemma 3 27B Instruct at Q3_K_M needs about 20.6 GB of the 32 GB usable on Beelink SER9 (Ryzen AI 9, 32 GB), leaving ~11.4 GB spare and running at ~5.7 tok/s (estimated), with room for about 16,384 tokens of context.
Which quantization of Gemma 3 should I use on Beelink SER9 (Ryzen AI 9, 32 GB)?
Q3_K_M — it needs about 20.6 GB of the 32 GB available, downloads as roughly 11.5 GB, and runs at an estimated 5.7 tokens/sec with up to 16K of context.
What limits Gemma 3 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 3n on Beelink SER9 (Ryzen AI 9, 32 GB)
- Gemma 4 on Beelink SER9 (Ryzen AI 9, 32 GB)
- GLM-4.7 / GLM-Z1 on Beelink SER9 (Ryzen AI 9, 32 GB)
- GLM-5 / GLM-5.1 on Beelink SER9 (Ryzen AI 9, 32 GB)
- GLM-6 on Beelink SER9 (Ryzen AI 9, 32 GB)
Gemma 3 on GPUs
- Gemma 3 on NVIDIA GeForce RTX 5090
- Gemma 3 on NVIDIA GeForce RTX 5080
- Gemma 3 on NVIDIA GeForce RTX 5070 Ti
- Gemma 3 on NVIDIA GeForce RTX 5070