Can I Run Phi-4 Mini on Beelink SER9 (Ryzen AI 9, 32 GB)?
Written by Jakub Rusinowski · Last updated February 4, 2025
Yes — comfortably
Yes, comfortably — Phi-4 Mini (3.8B) at Q8_0 needs about 5.9 GB of the 32 GB usable on Beelink SER9 (Ryzen AI 9, 32 GB), leaving ~26.1 GB spare and running at ~18.8 tok/s (estimated), with room for about 65,536 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~18.8 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) |
Phi-4 Mini on Beelink SER9 (Ryzen AI 9, 32 GB): memory by quantization
| Quant | Memory needed | Fits 32 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 9.5 GB | ✓ Yes | 64K | ~10.8 tok/s | 7.6 GB |
| Q8_0 | 5.9 GB | ✓ Yes | 64K | ~18.8 tok/s | 4 GB |
| Q6_K | 5 GB | ✓ Yes | 64K | ~23.3 tok/s | 3.1 GB |
| Q5_K_M | 4.6 GB | ✓ Yes | 64K | ~26.2 tok/s | 2.7 GB |
| Q4_K_M | 4.2 GB | ✓ Yes | 64K | ~29.7 tok/s | 2.3 GB |
| Q3_K_M | 3.5 GB | ✓ Yes | 64K | ~38.2 tok/s | 1.6 GB |
| Q2_K | 3.1 GB | ✓ Yes | 64K | ~45.3 tok/s | 1.2 GB |
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 Phi-4 Mini on Beelink SER9 (Ryzen AI 9, 32 GB)?
Yes, comfortably — Phi-4 Mini (3.8B) at Q8_0 needs about 5.9 GB of the 32 GB usable on Beelink SER9 (Ryzen AI 9, 32 GB), leaving ~26.1 GB spare and running at ~18.8 tok/s (estimated), with room for about 65,536 tokens of context.
Which quantization of Phi-4 Mini should I use on Beelink SER9 (Ryzen AI 9, 32 GB)?
Q8_0 — it needs about 5.9 GB of the 32 GB available, downloads as roughly 4 GB, and runs at an estimated 18.8 tokens/sec with up to 64K of context.
What limits Phi-4 Mini 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)
- Poolside Laguna XS 2.1 on Beelink SER9 (Ryzen AI 9, 32 GB)
- Qwen 2.5 Family on Beelink SER9 (Ryzen AI 9, 32 GB)
- Qwen 2.5 VL on Beelink SER9 (Ryzen AI 9, 32 GB)
- Qwen 3 on Beelink SER9 (Ryzen AI 9, 32 GB)
- Qwen 3.5 on Beelink SER9 (Ryzen AI 9, 32 GB)
Phi-4 Mini on GPUs
- Phi-4 Mini on NVIDIA GeForce RTX 5060 Ti 8GB
- Phi-4 Mini on NVIDIA GeForce RTX 5060
- Phi-4 Mini on NVIDIA GeForce RTX 4060
What This Model Is Good At
Model & Tools
← Can I Run It? | Phi-4 Mini model page | Check your hardware