Can I Run Qwen3-Coder on Beelink SER9 (Ryzen AI 9, 32 GB)?
Autor: Jakub Rusinowski · Ostatnia aktualizacja: 29 września 2026
Yes
Yes — Qwen3-Coder 30B-A3B (MoE) at Q6_K needs about 26.6 GB of the 32 GB usable on Beelink SER9 (Ryzen AI 9, 32 GB) (~5.4 GB spare), at ~27.2 tok/s (estimated), with room for about 32,768 tokens of context.
Confidence: high · Recommended quantization: Q6_K · Estimated speed: ~27.2 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) |
Qwen3-Coder on Beelink SER9 (Ryzen AI 9, 32 GB): memory by quantization
| Quant | Memory needed | Fits 32 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 62.6 GB | ✗ No | — | — | 61 GB |
| Q8_0 | 34 GB | ✗ No | — | — | 32.4 GB |
| Q6_K | 26.6 GB | ✓ Yes | 32K | ~27.2 tok/s | 25 GB |
| Q5_K_M | 23.2 GB | ✓ Yes | 64K | ~30.6 tok/s | 21.6 GB |
| Q4_K_M | 20 GB | ✓ Yes | 64K | ~34.7 tok/s | 18.4 GB |
| Q3_K_M | 14.6 GB | ✓ Yes | 128K | ~44.7 tok/s | 13 GB |
| Q2_K | 11.6 GB | ✓ Yes | 128K | ~53.3 tok/s | 10 GB |
Which Qwen3-Coder sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Qwen3-Coder 480B-A35B (MoE) | 291.6 GB | ✗ Too large | — |
| Qwen3-Coder-Next (80B-A3B MoE) | 49.4 GB | ✗ Too large | — |
| Qwen3-Coder 30B-A3B (MoE) | 20 GB | ✓ Fits | ~34.7 tok/s |
What to watch out for
- 2 larger variants of Qwen3-Coder do not fit and would need CPU offload or different hardware.
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 Qwen3-Coder on Beelink SER9 (Ryzen AI 9, 32 GB)?
Yes — Qwen3-Coder 30B-A3B (MoE) at Q6_K needs about 26.6 GB of the 32 GB usable on Beelink SER9 (Ryzen AI 9, 32 GB) (~5.4 GB spare), at ~27.2 tok/s (estimated), with room for about 32,768 tokens of context.
Which quantization of Qwen3-Coder should I use on Beelink SER9 (Ryzen AI 9, 32 GB)?
Q6_K — it needs about 26.6 GB of the 32 GB available, downloads as roughly 25 GB, and runs at an estimated 27.2 tokens/sec with up to 32K of context.
What limits Qwen3-Coder 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)
- SmolLM2 on Beelink SER9 (Ryzen AI 9, 32 GB)
- SmolLM3 on Beelink SER9 (Ryzen AI 9, 32 GB)
- StarCoder 2 on Beelink SER9 (Ryzen AI 9, 32 GB)
- Aya 3B (Tiny Aya) on Beelink SER9 (Ryzen AI 9, 32 GB)
- VibeThinker on Beelink SER9 (Ryzen AI 9, 32 GB)
Qwen3-Coder on GPUs
- Qwen3-Coder on NVIDIA GeForce RTX 5070
- Qwen3-Coder on NVIDIA GeForce RTX 5060 Ti 8GB
- Qwen3-Coder on NVIDIA GeForce RTX 5060
- Qwen3-Coder on NVIDIA GeForce RTX 4070 Ti
What This Model Is Good At
Model & Tools
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