Can I Run Nemotron Cascade 2 on Beelink SER9 (Ryzen AI 9, 32 GB)?

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 11 września 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)

Nemotron Cascade 2 on Beelink SER9 (Ryzen AI 9, 32 GB): memory by quantization

Quant	Memory needed	Fits 32 GB?	Max context	Est. speed	Download
F16	65.8 GB	✗ No	—	—	63.2 GB
Q8_0	36.2 GB	✗ No	—	—	33.6 GB
Q6_K	28.5 GB	✓ Yes	16K	~45.6 tok/s	25.9 GB
Q5_K_M	25 GB	✓ Yes	32K	~50.3 tok/s	22.4 GB
Q4_K_M	21.7 GB	✓ Yes	32K	~55.6 tok/s	19.1 GB
Q3_K_M	16.1 GB	✓ Yes	64K	~67.9 tok/s	13.5 GB
Q2_K	13 GB	✓ Yes	64K	~77.2 tok/s	10.4 GB

Which Nemotron Cascade 2 sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Nemotron Cascade 2 70B (Unverified Listing)	45.4 GB	✗ Too large	—
Nemotron-Cascade 2 30B-A3B	21.7 GB	✓ Fits	~55.6 tok/s

What to watch out for

• 1 larger variant of Nemotron Cascade 2 does not fit and would need CPU offload or different hardware.
• 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 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 inferred from the parameter count because this model does not publish its full attention configuration.

FAQ

Can I run Nemotron Cascade 2 on Beelink SER9 (Ryzen AI 9, 32 GB)?

Yes — Nemotron-Cascade 2 30B-A3B at Q6_K needs about 28.5 GB of the 32 GB usable on Beelink SER9 (Ryzen AI 9, 32 GB) (~3.5 GB spare), at ~45.6 tok/s (estimated), with room for about 16,384 tokens of context.

Which quantization of Nemotron Cascade 2 should I use on Beelink SER9 (Ryzen AI 9, 32 GB)?

Q6_K — it needs about 28.5 GB of the 32 GB available, downloads as roughly 25.9 GB, and runs at an estimated 45.6 tokens/sec with up to 16K of context.

What limits Nemotron Cascade 2 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

• Nemotron Cascade 2 on Mac mini M4 Pro 64 GB
• Nemotron Cascade 2 on Framework Desktop 128 GB

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

• Nex-N2 on Beelink SER9 (Ryzen AI 9, 32 GB)
• Nex-N2.5 on Beelink SER9 (Ryzen AI 9, 32 GB)
• North Mini Code on Beelink SER9 (Ryzen AI 9, 32 GB)
• OLMo 2 on Beelink SER9 (Ryzen AI 9, 32 GB)
• Phi 3.5 Family on Beelink SER9 (Ryzen AI 9, 32 GB)

Nemotron Cascade 2 on GPUs

• Nemotron Cascade 2 on NVIDIA GeForce RTX 5090
• Nemotron Cascade 2 on NVIDIA GeForce RTX 5080
• Nemotron Cascade 2 on NVIDIA GeForce RTX 5070 Ti
• Nemotron Cascade 2 on NVIDIA GeForce RTX 5070

What This Model Is Good At

• Best local LLMs for reasoning
• Best local LLMs for rag
• Best local LLMs for agents

Model & Tools

• Nemotron Cascade 2 model page
• VRAM calculator
• Check your own hardware

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