Can I Run Qwen 2.5 VL on 16 GB system 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Superseded model. Qwen 2.5 VL has been superseded by Qwen 3.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 →

作者： Jakub Rusinowski · 最后更新： 2025年1月25日

Yes

Yes — Qwen 2.5 VL 7B Instruct at Q8_0 needs about 10.1 GB of the 12.8 GB usable on 16 GB system RAM (~2.7 GB spare), at ~7.3 tok/s (estimated), with room for about 32,768 tokens of context.

Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~7.3 tok/s

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16 GB system RAM — what it gives a model

Usable memory for models	12.8 GB
Memory bandwidth	90 GB/s

Qwen 2.5 VL on 16 GB system RAM: memory by quantization

Quant	Memory needed	Fits 12.8 GB?	Max context	Est. speed	Download
F16	17.8 GB	✗ No	—	—	16.6 GB
Q8_0	10.1 GB	✓ Yes	32K	~7.3 tok/s	8.8 GB
Q6_K	8.1 GB	✓ Yes	64K	~9.4 tok/s	6.8 GB
Q5_K_M	7.1 GB	✓ Yes	64K	~10.7 tok/s	5.9 GB
Q4_K_M	6.3 GB	✓ Yes	64K	~12.5 tok/s	5 GB
Q3_K_M	4.8 GB	✓ Yes	64K	~17.1 tok/s	3.5 GB
Q2_K	4 GB	✓ Yes	64K	~21.6 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	~12.5 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.
These figures assume CPU-only inference. Any discrete GPU, even an 8 GB one, will be several times faster for models that fit in its VRAM.

Recommended setup

llama.cpp (CPU build) or Ollama — both run without a GPU

How these numbers are calculated

Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
12.8 GB of the 16 GB is treated as usable for model weights (80% — the rest is the OS and running applications).
DDR5-5600 dual channel at 89.6 GB/s peak. CPU decode is assumed to sustain 35% of that peak, because CPU inference is not purely bandwidth-bound — it also spends real time in compute and thread synchronisation. This figure is an assumption, not a fitted constant: no CPU measurement is in the calibration set.
CPU-only inference: no GPU is assumed. A GPU of any size will beat these figures substantially.
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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