Can I Run Phi-4 Mini on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)?
Written by Jakub Rusinowski · Last updated February 4, 2025
Yes — comfortably
Yes, comfortably — Phi-4 Mini (3.8B) at Q8_0 needs about 5.9 GB of the 8 GB usable on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM), leaving ~2.1 GB spare and running at ~38.4 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~38.4 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 | 256 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 Mini 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 | 9.5 GB | ✗ No | — | — | 7.6 GB |
| Q8_0 | 5.9 GB | ✓ Yes | 16K | ~38.4 tok/s | 4 GB |
| Q6_K | 5 GB | ✓ Yes | 16K | ~47 tok/s | 3.1 GB |
| Q5_K_M | 4.6 GB | ✓ Yes | 32K | ~52.4 tok/s | 2.7 GB |
| Q4_K_M | 4.2 GB | ✓ Yes | 32K | ~58.9 tok/s | 2.3 GB |
| Q3_K_M | 3.5 GB | ✓ Yes | 32K | ~74.2 tok/s | 1.6 GB |
| Q2_K | 3.1 GB | ✓ Yes | 32K | ~86.6 tok/s | 1.2 GB |
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 Laptop GPU at 256 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 Mini on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)?
Yes, comfortably — Phi-4 Mini (3.8B) at Q8_0 needs about 5.9 GB of the 8 GB usable on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM), leaving ~2.1 GB spare and running at ~38.4 tok/s (estimated), with room for about 16,384 tokens of context.
Which quantization of Phi-4 Mini should I use on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)?
Q8_0 — it needs about 5.9 GB of the 8 GB available, downloads as roughly 4 GB, and runs at an estimated 38.4 tokens/sec with up to 16K of context.
What limits Phi-4 Mini 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
- Phi-4 Mini on MacBook Pro M4 Max 128 GB
- Phi-4 Mini on MacBook Pro M4 Max 48 GB
- Phi-4 Mini on MacBook Pro M4 Pro 24 GB
- Phi-4 Mini on MacBook Air M4 16 GB
Other Models on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)
- Qwen 2.5 Family on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)
- Qwen 2.5 VL on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)
- Qwen 3 on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)
- Qwen 3.5 on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)
- Qwen3-Coder on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)
Phi-4 Mini on GPUs
- Phi-4 Mini on NVIDIA GeForce RTX 5060 Ti 8GB
- Phi-4 Mini on NVIDIA GeForce RTX 5060
- Phi-4 Mini on NVIDIA GeForce RTX 4060
What This Model Is Good At
Model & Tools
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