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

Written by Jakub Rusinowski · Last updated September 6, 2026

Yes

Yes — Magistral Small 24B at Q3_K_M needs about 12.7 GB of the 16 GB usable on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM) (~3.3 GB spare), at ~28.4 tok/s (estimated), with room for about 16,384 tokens of context.

Confidence: medium · Recommended quantization: Q3_K_M · Estimated speed: ~28.4 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

Magistral Small 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	50.5 GB	✗ No	—	—	48 GB
Q8_0	28 GB	✗ No	—	—	25.5 GB
Q6_K	22.2 GB	✗ No	—	—	19.7 GB
Q5_K_M	19.5 GB	✗ No	—	—	17 GB
Q4_K_M	17 GB	✗ No	—	—	14.5 GB
Q3_K_M	12.7 GB	✓ Yes	16K	~28.4 tok/s	10.2 GB
Q2_K	10.4 GB	✓ Yes	32K	~35.4 tok/s	7.9 GB

What to watch out for

Q3_K_M is the only quantization that fits, and it is a heavily degraded one — expect noticeably worse output than the same model at Q4_K_M. A smaller model at Q4 is usually the better trade.
1 larger variant of Magistral Small does not fit and would need CPU offload or different hardware.
This model does not publish its full attention configuration, so the KV-cache share of these figures is inferred from its parameter count rather than computed exactly.

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 inferred from the parameter count because this model does not publish its full attention configuration.

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