Written by Jakub Rusinowski · Last updated November 12, 2024
Yes
Yes — Qwen 2.5 14B Instruct at Q6_K needs about 13.9 GB of the 16 GB usable on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM) (~2.1 GB spare), at ~25.8 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: high · Recommended quantization: Q6_K · Estimated speed: ~25.8 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 | 30.4 GB | ✗ No | — | — | 28 GB |
| Q8_0 | 17.3 GB | ✗ No | — | — | 14.9 GB |
| Q6_K | 13.9 GB | ✓ Yes | 16K | ~25.8 tok/s | 11.5 GB |
| Q5_K_M | 12.3 GB | ✓ Yes | 16K | ~29.3 tok/s | 9.9 GB |
| Q4_K_M | 10.9 GB | ✓ Yes | 32K | ~33.6 tok/s | 8.5 GB |
| Q3_K_M | 8.4 GB | ✓ Yes | 32K | ~44.6 tok/s | 6 GB |
| Q2_K | 7 GB | ✓ Yes | 32K | ~54.5 tok/s | 4.6 GB |
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Qwen 2.5 72B Instruct | 47 GB | ✗ Too large | — |
| Qwen 2.5 Coder 32B | 22.3 GB | ✗ Too large | — |
| Qwen 2.5 14B Instruct | 10.9 GB | ✓ Fits | ~33.6 tok/s |
| Qwen 2.5 7B Instruct | 5.9 GB | ✓ Fits | ~60.2 tok/s |
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Yes — Qwen 2.5 14B Instruct at Q6_K needs about 13.9 GB of the 16 GB usable on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM) (~2.1 GB spare), at ~25.8 tok/s (estimated), with room for about 16,384 tokens of context.
Q6_K — it needs about 13.9 GB of the 16 GB available, downloads as roughly 11.5 GB, and runs at an estimated 25.8 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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