Can I Run Mistral Family on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM)?

作者： Jakub Rusinowski · 最后更新： 2025年1月28日

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

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RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM) — what it gives a model

Usable memory for models	16 GB
Memory bandwidth	448 GB/s
Form factor	Desktop
Operating system	Windows or Linux
Memory upgradeable	Yes

Mistral Family on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM): memory by quantization

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

Superseded model. Mistral Family has been superseded by Mistral Small 4. This page is kept for reference; the newer family is a better starting point. Mistral Small 4 needs at least 75.5 GB at Q4_K_M with an 8K context, so it does not fit the RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM).

Which Mistral Family sizes fit

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

What to watch out for

Only ~1.1 GB of headroom at Q8_0: a longer context or a second application can push this into swapping.
1 larger variant of Mistral Family does not fit and would need CPU offload or different hardware.

RTX 5060 Ti 16 GB desktop limitations

The cheapest current 16 GB card, but its 448 GB/s bandwidth caps generation speed well below a 5080 on the same model.

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.
16 GB of VRAM on the NVIDIA GeForce RTX 5060 Ti 16GB at 448 GB/s.
32 GB of system RAM available for CPU offload when a model exceeds VRAM.
Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
KV cache is computed from this model's published attention configuration.

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