Can I Run Phi-4 Family on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)?
Written by Jakub Rusinowski · Last updated January 6, 2025
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
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
Can I run Phi-4 Family on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)?
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.
Which quantization of Phi-4 Family should I use on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)?
Q2_K — it needs about 7.1 GB of the 8 GB available, downloads as roughly 4.6 GB, and runs at an estimated 34.6 tokens/sec with up to 8K of context.
What limits Phi-4 Family on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)?
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Which runtime should I use?
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Other Computers
Other Models on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)
Phi-4 Family on GPUs
What This Model Is Good At
Model & Tools
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