Written by Jakub Rusinowski · Last updated March 20, 2026
Yes, but it is tight
Yes, but it is tight — Cogito v1 32B at Q6_K needs about 29.2 GB of the 32 GB usable on Beelink SER9 (Ryzen AI 9, 32 GB), leaving only ~2.8 GB before the runtime starts swapping. Expect ~6.9 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: medium · Recommended quantization: Q6_K · Estimated speed: ~6.9 tok/s
| 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) |
| Quant | Memory needed | Fits 32 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 66.9 GB | ✗ No | — | — | 64 GB |
| Q8_0 | 36.9 GB | ✗ No | — | — | 34 GB |
| Q6_K | 29.2 GB | ✓ Yes | 16K | ~6.9 tok/s | 26.2 GB |
| Q5_K_M | 25.6 GB | ✓ Yes | 16K | ~7.9 tok/s | 22.7 GB |
| Q4_K_M | 22.3 GB | ✓ Yes | 32K | ~9.2 tok/s | 19.3 GB |
| Q3_K_M | 16.6 GB | ✓ Yes | 64K | ~12.7 tok/s | 13.6 GB |
| Q2_K | 13.5 GB | ✓ Yes | 64K | ~16 tok/s | 10.5 GB |
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Cogito v1 70B | 45.7 GB | ✗ Too large | — |
| Cogito v1 32B | 22.3 GB | ✓ Fits | ~9.2 tok/s |
| Cogito v1 14B | 10.9 GB | ✓ Fits | ~19.8 tok/s |
| Cogito v1 8B | 6.7 GB | ✓ Fits | ~33.1 tok/s |
| Cogito v1 3B | 3.6 GB | ✓ Fits | ~70.8 tok/s |
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Yes, but it is tight — Cogito v1 32B at Q6_K needs about 29.2 GB of the 32 GB usable on Beelink SER9 (Ryzen AI 9, 32 GB), leaving only ~2.8 GB before the runtime starts swapping. Expect ~6.9 tok/s (estimated), with room for about 16,384 tokens of context.
Q6_K — it needs about 29.2 GB of the 32 GB available, downloads as roughly 26.2 GB, and runs at an estimated 6.9 tokens/sec with up to 16K of context.
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
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