1. Overview

Phi-4 is Microsoft’s latest language model in the Phi series, released in late 2024/early 2025. With approximately 14 billion parameters, Phi-4 is designed to deliver near-13B/14B intelligence with exceptional efficiency for local and edge inference.

2. Local Hardware Requirements

Phi-4 at 4-bit quantization requires 9-11GB of VRAM/RAM. It runs comfortably on 12GB GPUs (RTX 3060 12GB, RTX 4070) and Apple Silicon Macs with 16GB unified memory. On CPU-only systems with 16GB RAM, it generates 5-8 tokens/sec.

3. Real-World Capabilities

• General reasoning & Mathematics: Exceptional for its size, often beating larger models in logical and mathematical tasks.
• Coding: Strong code generation, though not quite at the level of Qwen 2.5 Coder or Llama 3.3 70B.
• Instruction Following: Excellent, thanks to Microsoft’s training focus on “textbook quality” data.

4. Strengths

Phi-4’s efficiency and reasoning capabilities are unmatched in the 13B-14B category. It is one of the best performance-per-GB models available locally.

5. Weaknesses

Multilingual performance is not as strong as Qwen or Llama 3.3. Context window is typically 128K, but some quantizations truncate earlier.

6. Comparison with Competing Local Models

Compared to Gemma 3 12B, Phi-4 has slightly better mathematical reasoning and instruction following, while Gemma excels in general multilingual tasks. Compared to Mistral Nemo 12B, Phi-4 is more focused on reasoning and less on agentic tool-use.

7. Who Should Run It?

• 12 GB GPU: Ideal choice for maximum intelligence within VRAM limits.
• CPU-only systems (16GB+ RAM): Best choice for CPU inference due to efficiency.
• Those prioritizing reasoning/math: Phi-4 is a top contender.

8. Verdict

Phi-4 is the best performance-per-GB model for consumer hardware, offering exceptional reasoning in a compact package.
Ratings:
Intelligence: 8.5/10
Coding: 7.5/10
Reasoning: 9/10
Local hardware efficiency: 9.5/10
Speed: 9/10
Ease of use: 9/10
Overall value: 9.5/10