Written by Jakub Rusinowski · Last updated January 28, 2025
Yes, but it is tight
Yes, but it is tight — Mistral NeMo 12B at Q8_0 needs about 14.9 GB of the 16 GB usable on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM), leaving only ~1.1 GB before the runtime starts swapping. Expect ~23.7 tok/s (estimated), with room for about 8,192 tokens of context.
Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~23.7 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 | 26.1 GB | ✗ No | — | — | 24 GB |
| Q8_0 | 14.9 GB | ✓ Yes | 8K | ~23.7 tok/s | 12.8 GB |
| Q6_K | 12 GB | ✓ Yes | 16K | ~29.8 tok/s | 9.8 GB |
| Q5_K_M | 10.6 GB | ✓ Yes | 32K | ~33.8 tok/s | 8.5 GB |
| Q4_K_M | 9.4 GB | ✓ Yes | 32K | ~38.8 tok/s | 7.2 GB |
| Q3_K_M | 7.3 GB | ✓ Yes | 32K | ~51.4 tok/s | 5.1 GB |
| Q2_K | 6.1 GB | ✓ Yes | 64K | ~62.6 tok/s | 3.9 GB |
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Mistral Small 3 (24B) | 17.2 GB | ✗ Too large | — |
| Mistral NeMo 12B | 9.4 GB | ✓ Fits | ~38.8 tok/s |
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Yes, but it is tight — Mistral NeMo 12B at Q8_0 needs about 14.9 GB of the 16 GB usable on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM), leaving only ~1.1 GB before the runtime starts swapping. Expect ~23.7 tok/s (estimated), with room for about 8,192 tokens of context.
Q8_0 — it needs about 14.9 GB of the 16 GB available, downloads as roughly 12.8 GB, and runs at an estimated 23.7 tokens/sec with up to 8K 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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