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

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 11 września 2026

Yes

Yes — Devstral Small 2505 24B at Q3_K_M needs about 12.4 GB of the 16 GB usable on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM) (~3.6 GB spare), at ~28.8 tok/s (estimated), with room for about 16,384 tokens of context.

Confidence: high · Recommended quantization: Q3_K_M · Estimated speed: ~28.8 tok/s

See what else this hardware can run →

Uruchom w chmurze RTX 4090 w RunPod — od $0.34/godz. · stawka sprawdzona 2026-07

lub porównaj w Vast.ai od $0.35/godz. (typical low · varies)

Jako partner Amazon zarabiamy na kwalifikujących się zakupach. Linki do chmurowych GPU są linkami polecającymi — możemy otrzymać prowizję bez dodatkowych kosztów dla Ciebie.

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

Devstral 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	50.1 GB	✗ No	—	—	48 GB
Q8_0	27.6 GB	✗ No	—	—	25.5 GB
Q6_K	21.8 GB	✗ No	—	—	19.7 GB
Q5_K_M	19.2 GB	✗ No	—	—	17 GB
Q4_K_M	16.6 GB	✗ No	—	—	14.5 GB
Q3_K_M	12.4 GB	✓ Yes	16K	~28.8 tok/s	10.2 GB
Q2_K	10 GB	✓ Yes	32K	~36.1 tok/s	7.9 GB

Which Devstral sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Devstral-2 123B	77.9 GB	✗ Too large	—
Devstral Small 2505 24B	16.6 GB	✗ Too large	—
Devstral Small 2 24B	16.6 GB	✗ Too large	—

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.
3 larger variants of Devstral do 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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