Can I Run Bonsai 27B on Beelink SER9 (Ryzen AI 9, 32 GB)?
Written by Jakub Rusinowski · Last updated July 15, 2026
Yes — comfortably
Yes, comfortably — 1-bit Bonsai 27B at Q4_K_M needs about 18.8 GB of the 32 GB usable on Beelink SER9 (Ryzen AI 9, 32 GB), leaving ~13.2 GB spare and running at ~5.2 tok/s (estimated), with room for about 65,536 tokens of context.
Confidence: medium · Recommended quantization: Q4_K_M · Estimated speed: ~5.2 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) |
Bonsai 27B on Beelink SER9 (Ryzen AI 9, 32 GB): memory by quantization
| Quant | Memory needed | Fits 32 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 56.5 GB | ✗ No | — | — | 54 GB |
| Q8_0 | 31.2 GB | ✓ Yes | 8K | ~3 tok/s | 28.7 GB |
| Q6_K | 24.7 GB | ✓ Yes | 32K | ~3.9 tok/s | 22.1 GB |
| Q5_K_M | 21.7 GB | ✓ Yes | 32K | ~4.5 tok/s | 19.1 GB |
| Q4_K_M | 18.8 GB | ✓ Yes | 64K | ~5.2 tok/s | 16.3 GB |
| Q3_K_M | 14.1 GB | ✓ Yes | 64K | ~7.2 tok/s | 11.5 GB |
| Q2_K | 11.4 GB | ✓ Yes | 64K | ~9 tok/s | 8.9 GB |
Which Bonsai 27B sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| 1-bit Bonsai 27B | 18.8 GB | ✓ Fits | ~5.2 tok/s |
| Ternary Bonsai 27B | 18.8 GB | ✓ Fits | ~5.2 tok/s |
What to watch out for
- This model does not publish its full attention configuration, so the KV-cache share of these figures is inferred from its parameter count rather than computed exactly.
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 inferred from the parameter count because this model does not publish its full attention configuration.
FAQ
Can I run Bonsai 27B on Beelink SER9 (Ryzen AI 9, 32 GB)?
Yes, comfortably — 1-bit Bonsai 27B at Q4_K_M needs about 18.8 GB of the 32 GB usable on Beelink SER9 (Ryzen AI 9, 32 GB), leaving ~13.2 GB spare and running at ~5.2 tok/s (estimated), with room for about 65,536 tokens of context.
Which quantization of Bonsai 27B should I use on Beelink SER9 (Ryzen AI 9, 32 GB)?
Q4_K_M — it needs about 18.8 GB of the 32 GB available, downloads as roughly 16.3 GB, and runs at an estimated 5.2 tokens/sec with up to 64K of context.
What limits Bonsai 27B 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)
- 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)
- Cosmos 3 on Beelink SER9 (Ryzen AI 9, 32 GB)
- DeepSeek-OCR on Beelink SER9 (Ryzen AI 9, 32 GB)
Bonsai 27B on GPUs
- Bonsai 27B on NVIDIA GeForce RTX 5070
- Bonsai 27B on NVIDIA GeForce RTX 5060 Ti 8GB
- Bonsai 27B on NVIDIA GeForce RTX 5060
- Bonsai 27B on NVIDIA GeForce RTX 4070 Ti
What This Model Is Good At
Model & Tools
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