Can I Run Ministral 3 on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)?

Written by Jakub Rusinowski · Last updated September 6, 2026

Yes — comfortably

Yes, comfortably — Ministral 3 14B at Q8_0 needs about 17.1 GB of the 32 GB usable on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM), leaving ~14.9 GB spare and running at ~72.3 tok/s (estimated), with room for about 65,536 tokens of context.

Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~72.3 tok/s

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RTX 5090 Desktop (32 GB VRAM, 64 GB RAM) — what it gives a model

Usable memory for models	32 GB
Memory bandwidth	1792 GB/s
Form factor	Desktop
Operating system	Windows or Linux
Memory upgradeable	Yes

Ministral 3 on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM): memory by quantization

Quant	Memory needed	Fits 32 GB?	Max context	Est. speed	Download
F16	30.2 GB	✓ Yes	16K	~42.4 tok/s	28 GB
Q8_0	17.1 GB	✓ Yes	64K	~72.3 tok/s	14.9 GB
Q6_K	13.7 GB	✓ Yes	64K	~88.5 tok/s	11.5 GB
Q5_K_M	12.1 GB	✓ Yes	64K	~98.6 tok/s	9.9 GB
Q4_K_M	10.6 GB	✓ Yes	128K	~110.5 tok/s	8.5 GB
Q3_K_M	8.1 GB	✓ Yes	128K	~138.9 tok/s	6 GB
Q2_K	6.8 GB	✓ Yes	128K	~161.8 tok/s	4.6 GB

Which Ministral 3 sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Ministral 3 14B	10.6 GB	✓ Fits	~110.5 tok/s
Ministral 3 8B	6.8 GB	✓ Fits	~159.6 tok/s
Ministral 3 3B	3.5 GB	✓ Fits	~256 tok/s

What to watch out for

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 5090 desktop limitations

575 W board power — budget for a 1000 W+ PSU and the heat it puts into the room.
Models larger than 32 GB must offload to system RAM, which costs roughly an order of magnitude in speed.

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.
32 GB of VRAM on the NVIDIA GeForce RTX 5090 at 1792 GB/s.
64 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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