Can I Run Phi-4 Family on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)?

作者： Jakub Rusinowski · 最后更新： 2025年1月6日

Yes

Yes — Phi-4 (14B) at Q2_K needs about 7.1 GB of the 8 GB usable on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM) (~0.9 GB spare), at ~34.6 tok/s (estimated), with room for about 8,192 tokens of context.

Confidence: high · Recommended quantization: Q2_K · Estimated speed: ~34.6 tok/s

Buy This HardwareIntel Arc B570 10GB — 10 GB VRAM · 150 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 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)

Phi-4 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	30.5 GB	✗ No	—	—	28 GB
Q8_0	17.4 GB	✗ No	—	—	14.9 GB
Q6_K	14 GB	✗ No	—	—	11.5 GB
Q5_K_M	12.4 GB	✗ No	—	—	9.9 GB
Q4_K_M	10.9 GB	✗ No	—	—	8.5 GB
Q3_K_M	8.4 GB	✗ No	—	—	6 GB
Q2_K	7.1 GB	✓ Yes	8K	~34.6 tok/s	4.6 GB

What to watch out for

Only ~0.9 GB of headroom at Q2_K: a longer context or a second application can push this into swapping.
Q2_K is the only quantization that fits, and it is a heavily degraded one — expect noticeably worse output than the same model at Q4_K_M. A smaller model at Q4 is usually the better trade.
1 larger variant of Phi-4 Family 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.

FAQ