Can I Run Llama 3.2 Family on Beelink SER9 (Ryzen AI 9, 32 GB)?

Written by Jakub Rusinowski · Last updated August 15, 2026

Beelink SER9 (Ryzen AI 9, 32 GB) — what it gives a model

Usable memory for models	32 GB
Memory bandwidth	256 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)

Llama 3.2 Family on Beelink SER9 (Ryzen AI 9, 32 GB): memory by quantization

Quant	Memory needed	Fits 32 GB?	Max context	Est. speed	Download
F16	23.3 GB	✓ Yes	32K	~8.6 tok/s	21.2 GB
Q8_0	13.4 GB	✓ Yes	64K	~15.5 tok/s	11.3 GB
Q6_K	10.8 GB	✓ Yes	64K	~19.6 tok/s	8.7 GB
Q5_K_M	9.7 GB	✓ Yes	64K	~22.3 tok/s	7.5 GB
Q4_K_M	8.5 GB	✓ Yes	64K	~25.6 tok/s	6.4 GB
Q3_K_M	6.7 GB	✓ Yes	64K	~34.2 tok/s	4.5 GB
Q2_K	5.6 GB	✓ Yes	64K	~41.9 tok/s	3.5 GB

Which Llama 3.2 Family sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Llama 3.2 90B Vision Instruct	57.8 GB	✗ Too large	—
Llama 3.2 11B Vision Instruct	8.5 GB	✓ Fits	~25.6 tok/s
Llama 3.2 3B Instruct	3.7 GB	✓ Fits	~67.6 tok/s
Llama 3.2 1B Instruct	1.8 GB	✓ Fits	~146.1 tok/s

What to watch out for

• 1 larger variant of Llama 3.2 Family does 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 256 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 Llama 3.2 Family on Beelink SER9 (Ryzen AI 9, 32 GB)?

Yes, comfortably — Llama 3.2 11B Vision Instruct at Q8_0 needs about 13.4 GB of the 32 GB usable on Beelink SER9 (Ryzen AI 9, 32 GB), leaving ~18.6 GB spare and running at ~15.5 tok/s (estimated), with room for about 65,536 tokens of context.

Which quantization of Llama 3.2 Family should I use on Beelink SER9 (Ryzen AI 9, 32 GB)?

Q8_0 — it needs about 13.4 GB of the 32 GB available, downloads as roughly 11.3 GB, and runs at an estimated 15.5 tokens/sec with up to 64K of context.

What limits Llama 3.2 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

• Llama 3.2 Family on Mac mini M4 16 GB
• Llama 3.2 Family on Mac mini M4 Pro 64 GB
• Llama 3.2 Family on Framework Desktop 128 GB

Other Models on Beelink SER9 (Ryzen AI 9, 32 GB)

• Llama 3.2 Vision on Beelink SER9 (Ryzen AI 9, 32 GB)
• Llama 3.3 on Beelink SER9 (Ryzen AI 9, 32 GB)
• Ministral on Beelink SER9 (Ryzen AI 9, 32 GB)
• Mistral Family on Beelink SER9 (Ryzen AI 9, 32 GB)
• Mistral Small 3.1 on Beelink SER9 (Ryzen AI 9, 32 GB)

Llama 3.2 Family on GPUs

• Llama 3.2 Family on NVIDIA GeForce RTX 5070
• Llama 3.2 Family on NVIDIA GeForce RTX 5060 Ti 8GB
• Llama 3.2 Family on NVIDIA GeForce RTX 5060
• Llama 3.2 Family on NVIDIA GeForce RTX 4070 Ti

What This Model Is Good At

• Best local LLMs for general assistant
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Model & Tools

• Llama 3.2 Family model page
• VRAM calculator
• Check your own hardware

← Can I Run It? | Llama 3.2 Family model page | Check your hardware