Written by Jakub Rusinowski · Last updated February 24, 2026
Yes — comfortably
Yes, comfortably — Qwen 3.5 9B at Q8_0 needs about 11.6 GB of the 16 GB usable on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM), leaving ~4.4 GB spare and running at ~30.8 tok/s (estimated), with room for about 32,768 tokens of context.
Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~30.8 tok/s
| Usable memory for models | 16 GB |
| Memory bandwidth | 448 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
| Quant | Memory needed | Fits 16 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 20 GB | ✗ No | — | — | 18 GB |
| Q8_0 | 11.6 GB | ✓ Yes | 32K | ~30.8 tok/s | 9.6 GB |
| Q6_K | 9.4 GB | ✓ Yes | 32K | ~38.4 tok/s | 7.4 GB |
| Q5_K_M | 8.4 GB | ✓ Yes | 32K | ~43.4 tok/s | 6.4 GB |
| Q4_K_M | 7.4 GB | ✓ Yes | 64K | ~49.5 tok/s | 5.4 GB |
| Q3_K_M | 5.8 GB | ✓ Yes | 64K | ~64.7 tok/s | 3.8 GB |
| Q2_K | 5 GB | ✓ Yes | 64K | ~77.8 tok/s | 3 GB |
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Qwen 3.5 397B-A17B | 244.7 GB | ✗ Too large | — |
| Qwen 3.5 122B-A10B | 77.3 GB | ✗ Too large | — |
| Qwen 3.5 35B-A3B | 23.8 GB | ✗ Too large | — |
| Qwen 3.5 27B | 18.8 GB | ✗ Too large | — |
| Qwen 3.5 9B | 7.4 GB | ✓ Fits | ~49.5 tok/s |
| Qwen 3.5 4B | 4.1 GB | ✓ Fits | ~92.8 tok/s |
| Qwen 3.5 2B | 2.7 GB | ✓ Fits | ~144.1 tok/s |
| Qwen 3.5 0.8B | 1.8 GB | ✓ Fits | ~223.1 tok/s |
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Yes, comfortably — Qwen 3.5 9B at Q8_0 needs about 11.6 GB of the 16 GB usable on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM), leaving ~4.4 GB spare and running at ~30.8 tok/s (estimated), with room for about 32,768 tokens of context.
Q8_0 — it needs about 11.6 GB of the 16 GB available, downloads as roughly 9.6 GB, and runs at an estimated 30.8 tokens/sec with up to 32K 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