Can I Run MiniCPM-V on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 11 września 2026

Yes — comfortably

Yes, comfortably — MiniCPM-V 4.5 at Q8_0 needs about 10.4 GB of the 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM), leaving ~5.6 GB spare and running at ~52.3 tok/s (estimated), with room for about 32,768 tokens of context.

Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~52.3 tok/s

Buy This HardwareIntel Arc B580 12GB — 12 GB VRAM · 190 W board power Deploy in the Cloud NowRTX 4090 on RunPod — from $0.34/hr · rate checked 2026-07

or compare on Vast.ai from $0.35/hr (typical low · varies)

As an Amazon Associate we earn from qualifying purchases. Cloud GPU links are referral links — we may earn a commission at no extra cost to you.

RTX 4090 Laptop (16 GB VRAM, 32 GB RAM) — what it gives a model

Usable memory for models	16 GB
Memory bandwidth	717 GB/s
Form factor	Laptop
Operating system	Windows or Linux
Memory upgradeable	Yes

MiniCPM-V on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM): memory by quantization

Quant	Memory needed	Fits 16 GB?	Max context	Est. speed	Download
F16	17.9 GB	✗ No	—	—	16 GB
Q8_0	10.4 GB	✓ Yes	32K	~52.3 tok/s	8.5 GB
Q6_K	8.5 GB	✓ Yes	32K	~64.5 tok/s	6.6 GB
Q5_K_M	7.6 GB	✓ Yes	32K	~72.2 tok/s	5.7 GB
Q4_K_M	6.8 GB	✓ Yes	32K	~81.4 tok/s	4.8 GB
Q3_K_M	5.4 GB	✓ Yes	32K	~103.6 tok/s	3.4 GB
Q2_K	4.6 GB	✓ Yes	32K	~122 tok/s	2.6 GB

Which MiniCPM-V sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
MiniCPM-V 4.5	6.8 GB	✓ Fits	~81.4 tok/s
MiniCPM-V 4.6	2.2 GB	✓ Fits	~232.6 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 4090 laptop limitations

A mobile RTX 4090 carries 16 GB, not the desktop card's 24 GB, and is closer to a desktop 4080 in throughput — which is why it is modelled against that chip here.
Sustained throughput depends on the chassis power limit; thin laptops throttle well below the quoted figures.

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 4080 at 717 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.

FAQ