Can I Run Yi 1.5 Family on Beelink SER9 (Ryzen AI 9, 32 GB)?
Written by Jakub Rusinowski · Last updated May 13, 2024
Yes — comfortably
Yes, comfortably — Yi 1.5 34B Chat at Q3_K_M needs about 17.5 GB of the 32 GB usable on Beelink SER9 (Ryzen AI 9, 32 GB), leaving ~14.5 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) |
Yi 1.5 Family on Beelink SER9 (Ryzen AI 9, 32 GB): memory by quantization
| Quant | Memory needed | Fits 32 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 71.6 GB | ✗ No | — | — | 68.8 GB |
| Q8_0 | 39.4 GB | ✗ No | — | — | 36.6 GB |
| Q6_K | 31 GB | ✓ Yes | 8K | ~3.1 tok/s | 28.2 GB |
| Q5_K_M | 27.2 GB | ✓ Yes | 16K | ~3.5 tok/s | 24.4 GB |
| Q4_K_M | 23.6 GB | ✓ Yes | 16K | ~4.1 tok/s | 20.8 GB |
| Q3_K_M | 17.5 GB | ✓ Yes | 16K | ~5.7 tok/s | 14.7 GB |
| Q2_K | 14.1 GB | ✓ Yes | 16K | ~7.2 tok/s | 11.3 GB |
Which Yi 1.5 Family sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Yi 1.5 34B Chat | 23.6 GB | ✓ Fits | ~4.1 tok/s |
| Yi 1.5 9B Chat | 6.9 GB | ✓ Fits | ~15.2 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 Yi 1.5 Family on Beelink SER9 (Ryzen AI 9, 32 GB)?
Yes, comfortably — Yi 1.5 34B Chat at Q3_K_M needs about 17.5 GB of the 32 GB usable on Beelink SER9 (Ryzen AI 9, 32 GB), leaving ~14.5 GB spare and running at ~5.7 tok/s (estimated), with room for about 16,384 tokens of context.
Which quantization of Yi 1.5 Family should I use on Beelink SER9 (Ryzen AI 9, 32 GB)?
Q3_K_M — it needs about 17.5 GB of the 32 GB available, downloads as roughly 14.7 GB, and runs at an estimated 5.7 tokens/sec with up to 16K of context.
What limits Yi 1.5 Family 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)
- Aya Expanse 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)
Yi 1.5 Family on GPUs
- Yi 1.5 Family on NVIDIA GeForce RTX 5090
- Yi 1.5 Family on NVIDIA GeForce RTX 5070
- Yi 1.5 Family on NVIDIA GeForce RTX 5060 Ti 8GB
- Yi 1.5 Family on NVIDIA GeForce RTX 5060
What This Model Is Good At
Model & Tools
← Can I Run It? | Yi 1.5 Family model page | Check your hardware