Can I Run Qwen 2.5 VL on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?

Superseded model. Qwen 2.5 VL has been superseded by Qwen 3. This page is kept for reference; the newer family is a better starting point. View Qwen 3 →

Written by Jakub Rusinowski · Last updated January 25, 2025

Yes — comfortably

Yes, comfortably — Qwen 2.5 VL 7B Instruct at Q8_0 needs about 10.1 GB of the 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM), leaving ~5.9 GB spare and running at ~52.5 tok/s (estimated), with room for about 65,536 tokens of context.

Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~52.5 tok/s

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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

Qwen 2.5 VL 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	17.8 GB	✗ No	—	—	16.6 GB
Q8_0	10.1 GB	✓ Yes	64K	~52.5 tok/s	8.8 GB
Q6_K	8.1 GB	✓ Yes	64K	~65.3 tok/s	6.8 GB
Q5_K_M	7.1 GB	✓ Yes	64K	~73.5 tok/s	5.9 GB
Q4_K_M	6.3 GB	✓ Yes	64K	~83.4 tok/s	5 GB
Q3_K_M	4.8 GB	✓ Yes	64K	~108.1 tok/s	3.5 GB
Q2_K	4 GB	✓ Yes	64K	~129 tok/s	2.7 GB

Which Qwen 2.5 VL sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Qwen 2.5 VL 72B Instruct	47.8 GB	✗ Too large	—
Qwen 2.5 VL 7B Instruct	6.3 GB	✓ Fits	~83.4 tok/s

What to watch out for

1 larger variant of Qwen 2.5 VL does not fit and would need CPU offload or different hardware.

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 computed from this model's published attention configuration.

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