Can I Run GLM-5 / GLM-5.1 on Beelink SER9 (Ryzen AI 9, 32 GB)?
Autor: Jakub Rusinowski · Ostatnia aktualizacja: 1 maja 2026
No
No — GLM-5 / GLM-5.1 does not fit in Beelink SER9 (Ryzen AI 9, 32 GB). Even at Q2_K it needs about 250.6 GB against 32 GB usable, a shortfall of roughly 218.6 GB.
Confidence: medium
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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) |
GLM-5 / GLM-5.1 on Beelink SER9 (Ryzen AI 9, 32 GB): memory by quantization
| Quant | Memory needed | Fits 32 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 1494 GB | ✗ No | — | — | 1488 GB |
| Q8_0 | 796.5 GB | ✗ No | — | — | 790.5 GB |
| Q6_K | 616.1 GB | ✗ No | — | — | 610.1 GB |
| Q5_K_M | 533.3 GB | ✗ No | — | — | 527.3 GB |
| Q4_K_M | 455.2 GB | ✗ No | — | — | 449.2 GB |
| Q3_K_M | 323.2 GB | ✗ No | — | — | 317.1 GB |
| Q2_K | 250.6 GB | ✗ No | — | — | 244.6 GB |
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 GLM-5 / GLM-5.1 on Beelink SER9 (Ryzen AI 9, 32 GB)?
No — GLM-5 / GLM-5.1 does not fit in Beelink SER9 (Ryzen AI 9, 32 GB). Even at Q2_K it needs about 250.6 GB against 32 GB usable, a shortfall of roughly 218.6 GB.
What limits GLM-5 / GLM-5.1 on Beelink SER9 (Ryzen AI 9, 32 GB)?
Memory. Beelink SER9 (Ryzen AI 9, 32 GB) provides about 32 GB usable, and the smallest quantization of this model needs 250.6 GB.
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)
- GPT-OSS on Beelink SER9 (Ryzen AI 9, 32 GB)
- Granite 3.0 on Beelink SER9 (Ryzen AI 9, 32 GB)
- IBM Granite 4.0 on Beelink SER9 (Ryzen AI 9, 32 GB)
- IBM Granite 4.1 on Beelink SER9 (Ryzen AI 9, 32 GB)
- IBM Granite 4.2 on Beelink SER9 (Ryzen AI 9, 32 GB)
GLM-5 / GLM-5.1 on GPUs
- GLM-5 / GLM-5.1 on NVIDIA GeForce RTX 5090
- GLM-5 / GLM-5.1 on NVIDIA GeForce RTX 5070
- GLM-5 / GLM-5.1 on NVIDIA GeForce RTX 5060 Ti 8GB
- GLM-5 / GLM-5.1 on NVIDIA GeForce RTX 5060
What This Model Is Good At
- Best local LLMs for coding
- Best local LLMs for document analysis
- Best local LLMs for enterprise assistant
Model & Tools
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