Phi-4 vs Gemma 3 vs Qwen 3 Mini: Edge AI Models Ranked
The three best small models for edge deployment compared. We test Phi-4, Gemma 3, and Qwen 3 Mini on performance, efficiency, and on-device capabilities.
The Edge AI Revolution
Running AI models locally—on laptops, phones, and edge devices—eliminates latency, reduces costs, ensures privacy, and enables offline operation. Microsoft's Phi-4, Google's Gemma 3, and Alibaba's Qwen 3 Mini represent the state of the art in efficient small models.
We benchmarked all three on identical hardware (MacBook Pro M3, RTX 4070 laptop, Raspberry Pi 5) to provide practical deployment guidance.
Performance Benchmarks
MMLU: Phi-4 (82%), Gemma 3 (80%), Qwen 3 Mini (79%). HumanEval: Phi-4 (74%), Qwen 3 Mini (72%), Gemma 3 (70%). Reasoning: Phi-4 leads, followed closely by Qwen.
Phi-4's edge comes from Microsoft's training methodology—high-quality synthetic data and curriculum learning produce better per-parameter performance. All three are remarkably capable for their size.
Inference Speed
On MacBook Pro M3 (quantized Q4): Gemma 3 leads at 55 tokens/s, Phi-4 at 48 tokens/s, Qwen 3 Mini at 52 tokens/s. On RTX 4070: Phi-4 at 95 tokens/s, Gemma 3 at 88 tokens/s, Qwen 3 Mini at 91 tokens/s.
All three are fast enough for interactive applications on modern hardware. Gemma 3's architecture is slightly more efficient for CPU-only inference, making it better for Apple Silicon Macs.
Memory & Resource Usage
At Q4 quantization: Phi-4 (14B) uses 8.5GB RAM, Gemma 3 (9B) uses 5.8GB RAM, Qwen 3 Mini (7B) uses 4.2GB RAM. Smaller models enable deployment on more constrained devices.
For mobile and IoT deployment, Qwen 3 Mini's smaller footprint is a significant advantage. Phi-4's larger size pays for itself in quality on devices that can support it.
Multilingual Capability
Qwen 3 Mini leads decisively on multilingual tasks, particularly CJK languages (Chinese, Japanese, Korean) and Arabic. Its training data includes significantly more non-English content.
Gemma 3 has good multilingual support across European languages. Phi-4 is English-dominant but capable in major world languages.
Deployment & Ecosystem
All three work with Ollama, llama.cpp, and ONNX Runtime. Gemma 3 has the best Google ecosystem integration (Android, Chrome, MediaPipe). Phi-4 integrates well with Windows and Azure. Qwen has strong HuggingFace community support.
Choose based on your target platform: Gemma for Android/Chrome, Phi-4 for Windows/Azure, Qwen for cross-platform multilingual needs.
Final Ranking
1st: Phi-4 for overall quality. 2nd: Qwen 3 Mini for multilingual/constrained deployment. 3rd: Gemma 3 for Google ecosystem and CPU efficiency. All three are excellent choices.
Explore edge and cloud AI models on Vincony.com—compare outputs and find the right model for your deployment target.