Can I Run Qwen 2.5 Family on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)?

Superseded model. Qwen 2.5 Family has been superseded by Qwen 3.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 November 12, 2024

Yes, but it is tight

Yes, but it is tight — Qwen 2.5 7B Instruct at Q6_K needs about 7.5 GB of the 8 GB usable on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM), leaving only ~0.5 GB before the runtime starts swapping. Expect ~29.5 tok/s (estimated), with room for about 16,384 tokens of context.

Confidence: medium · Recommended quantization: Q6_K · Estimated speed: ~29.5 tok/s

RTX 4060 Laptop (8 GB VRAM, 16 GB RAM) — what it gives a model

Usable memory for models	8 GB
Memory bandwidth	272 GB/s
Form factor	Laptop
Operating system	Windows or Linux
Memory upgradeable	Yes
Price	$1,099 (lib/data/laptops.ts (street price), checked 2026-07-06)

Qwen 2.5 Family on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM): memory by quantization

Quant	Memory needed	Fits 8 GB?	Max context	Est. speed	Download
F16	16.5 GB	✗ No	—	—	15.2 GB
Q8_0	9.3 GB	✗ No	—	—	8.1 GB
Q6_K	7.5 GB	✓ Yes	16K	~29.5 tok/s	6.2 GB
Q5_K_M	6.7 GB	✓ Yes	16K	~33.6 tok/s	5.4 GB
Q4_K_M	5.9 GB	✓ Yes	32K	~38.6 tok/s	4.6 GB
Q3_K_M	4.5 GB	✓ Yes	64K	~51.9 tok/s	3.2 GB
Q2_K	3.8 GB	✓ Yes	64K	~63.9 tok/s	2.5 GB

Which Qwen 2.5 Family sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Qwen 2.5 72B Instruct	47 GB	✗ Too large	—
Qwen 2.5 Coder 32B	22.3 GB	✗ Too large	—
Qwen 2.5 14B Instruct	10.9 GB	✗ Too large	—
Qwen 2.5 7B Instruct	5.9 GB	✓ Fits	~38.6 tok/s

What to watch out for

Only ~0.5 GB of headroom at Q6_K: a longer context or a second application can push this into swapping.
3 larger variants of Qwen 2.5 Family do not fit and would need CPU offload or different hardware.

RTX 4060 laptop limitations

8 GB VRAM limits you to 7–8B models at Q4 with a short context.
System RAM is usually upgradeable on this class of laptop even though VRAM is not.

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.
8 GB of VRAM on the NVIDIA GeForce RTX 4060 at 272 GB/s.
16 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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