Can I Run EXAONE 3.5 on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)?

作者： Jakub Rusinowski · 最后更新： 2026年2月10日

Yes, but it is tight

Yes, but it is tight — EXAONE 3.5 7.8B at Q5_K_M needs about 7.4 GB of the 8 GB usable on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM), leaving only ~0.6 GB before the runtime starts swapping. Expect ~31.3 tok/s (estimated), with room for about 8,192 tokens of context.

Confidence: medium · Recommended quantization: Q5_K_M · Estimated speed: ~31.3 tok/s

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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)

EXAONE 3.5 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	17.5 GB	✗ No	—	—	15.6 GB
Q8_0	10.2 GB	✗ No	—	—	8.3 GB
Q6_K	8.3 GB	✗ No	—	—	6.4 GB
Q5_K_M	7.4 GB	✓ Yes	8K	~31.3 tok/s	5.5 GB
Q4_K_M	6.6 GB	✓ Yes	16K	~35.8 tok/s	4.7 GB
Q3_K_M	5.2 GB	✓ Yes	16K	~47.2 tok/s	3.3 GB
Q2_K	4.4 GB	✓ Yes	32K	~57.3 tok/s	2.6 GB

Which EXAONE 3.5 sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
EXAONE 3.5 32B	22.3 GB	✗ Too large	—
EXAONE 3.5 7.8B	6.6 GB	✓ Fits	~35.8 tok/s
EXAONE 3.5 2.4B	2.9 GB	✓ Fits	~91.8 tok/s

What to watch out for

Only ~0.6 GB of headroom at Q5_K_M: a longer context or a second application can push this into swapping.
1 larger variant of EXAONE 3.5 does 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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