Can I Run Aya Expanse on Beelink SER9 (Ryzen AI 9, 32 GB)?
Written by Jakub Rusinowski · Last updated October 8, 2024
Yes — comfortably
Yes, comfortably — Aya Expanse 32B at Q3_K_M needs about 15.9 GB of the 32 GB usable on Beelink SER9 (Ryzen AI 9, 32 GB), leaving ~16.1 GB spare and running at ~6.1 tok/s (estimated), with room for about 65,536 tokens of context.
Confidence: high · 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) |
Aya Expanse on Beelink SER9 (Ryzen AI 9, 32 GB): memory by quantization
| Quant | Memory needed | Fits 32 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 66.7 GB | ✗ No | — | — | 64.6 GB |
| Q8_0 | 36.5 GB | ✗ No | — | — | 34.3 GB |
| Q6_K | 28.6 GB | ✓ Yes | 16K | ~3.3 tok/s | 26.5 GB |
| Q5_K_M | 25 GB | ✓ Yes | 32K | ~3.8 tok/s | 22.9 GB |
| Q4_K_M | 21.6 GB | ✓ Yes | 64K | ~4.4 tok/s | 19.5 GB |
| Q3_K_M | 15.9 GB | ✓ Yes | 64K | ~6.1 tok/s | 13.8 GB |
| Q2_K | 12.8 GB | ✓ Yes | 64K | ~7.8 tok/s | 10.6 GB |
Which Aya Expanse sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Aya Expanse 32B | 21.6 GB | ✓ Fits | ~4.4 tok/s |
| Aya Expanse 8B | 6.7 GB | ✓ Fits | ~16.1 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 Aya Expanse on Beelink SER9 (Ryzen AI 9, 32 GB)?
Yes, comfortably — Aya Expanse 32B at Q3_K_M needs about 15.9 GB of the 32 GB usable on Beelink SER9 (Ryzen AI 9, 32 GB), leaving ~16.1 GB spare and running at ~6.1 tok/s (estimated), with room for about 65,536 tokens of context.
Which quantization of Aya Expanse should I use on Beelink SER9 (Ryzen AI 9, 32 GB)?
Q3_K_M — it needs about 15.9 GB of the 32 GB available, downloads as roughly 13.8 GB, and runs at an estimated 6.1 tokens/sec with up to 64K of context.
What limits Aya Expanse 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)
- BitNet b1.58 on Beelink SER9 (Ryzen AI 9, 32 GB)
- Bonsai 27B on Beelink SER9 (Ryzen AI 9, 32 GB)
- Codestral on Beelink SER9 (Ryzen AI 9, 32 GB)
- Cogito v1 on Beelink SER9 (Ryzen AI 9, 32 GB)
- Command R Family on Beelink SER9 (Ryzen AI 9, 32 GB)
Aya Expanse on GPUs
- Aya Expanse on NVIDIA GeForce RTX 5090
- Aya Expanse on NVIDIA GeForce RTX 5070
- Aya Expanse on NVIDIA GeForce RTX 5060 Ti 8GB
- Aya Expanse on NVIDIA GeForce RTX 5060
What This Model Is Good At
- Best local LLMs for document analysis
- Best local LLMs for translation
- Best local LLMs for enterprise assistant
Model & Tools
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