Phi-4 Mini — Local AI Model by Microsoft

作者： Jakub Rusinowski · 最后更新： 2026年7月10日

Microsoft's ultra-efficient small model for mobile and edge deployment. Phi-4 Mini achieves remarkable reasoning capabilities in just 3.8B parameters using high-quality synthetic data — the same approach behind Phi-4 14B. Designed for on-device AI on phones and laptops without discrete GPUs.

Hardware Requirements

Phi-4 Mini (3.8B)Min 3 GB VRAM · Q4_K_M · 128,000 ctx · ollama run phi4-mini

Recommended GPU

The cheapest GPU that runs Phi-4 Mini locally (min 3 GB VRAM) is the Intel Arc B570 (10 GB).

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Intel Arc B570 10GB
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How to Run Locally

Install Ollama then run: ollama run phi4-mini

Minimum VRAM: 3 GB. For best results use Q4_K_M quantization.

Phi-4 Mini — Frequently Asked Questions

How much VRAM does Phi-4 Mini need?

Phi-4 Mini needs about 3 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: Phi-4 Mini (3.8B) (3 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.

Can I run Phi-4 Mini on an RTX 4090 (24 GB)?

Yes — Phi-4 Mini runs on an RTX 4090 (24 GB) and other 24 GB cards such as the RTX 3090. Smaller variants also fit comfortably on 8–16 GB GPUs at Q4_K_M.

What quantization should I use for Phi-4 Mini?

Q4_K_M is the best balance of quality and VRAM for Phi-4 Mini in most cases. Choose Q8_0 for near-lossless quality if you have spare VRAM, or smaller quants (Q3/Q2) only when memory is tight.

How do I run Phi-4 Mini with Ollama?

Install Ollama, then run: ollama run phi4-mini. This downloads Phi-4 Mini and starts a local, OpenAI-compatible endpoint — no internet connection is needed after the initial download.