Yes — Phi-4 Mini (3.8B) runs at 95 tok/s on M3 with 16 GB RAM using Q4_K_M quantization via MLX. First token latency is 0.3s. Microsoft's efficient 3.8B model known for blazing speed on Apple Silicon.
Want to run Phi-4 Mini faster — or step up to bigger models? Find your Mac →The LLMCheck index estimates Phi-4 Mini on M3 using our published methodology: Q4_K_M quantization, memory-bandwidth scaling, and cross-referenced third-party benchmarks where available. Figures are transparent estimates — own this config? Submit a real benchmark →
| Metric | Value |
|---|---|
| Tokens per second | 95 tok/s |
| Time to first token | 0.3s |
| Quantization | Q4_K_M |
| Minimum RAM | 16 GB |
| Recommended engine | MLX |
| Parameters | 3.8B |
| Benchmark date | 2026-02 |
Q4_K_M 3.8B MLX M3
The recommended engine for Phi-4 Mini on M3 is MLX. Install with pip and pull the model:
Alternatively, you can use Ollama for a simpler setup:
| Chip | Speed | First Token | Min RAM | Engine |
|---|---|---|---|---|
| M5 Max | 142 tok/s | 0.3s | 64 GB | Ollama |
| M4 Max | 125 tok/s | 0.3s | 48 GB | MLX |
| M4 Pro | 108 tok/s | 0.4s | 24 GB | Ollama |
| M2 | 72 tok/s | 0.5s | 8 GB | Ollama |
| M1 | 58 tok/s | 0.6s | 16 GB | Ollama |
To run Phi-4 Mini on M3 you need:
Phi-4 Mini needs about 16 GB of unified memory. These current Apple Silicon Macs have the headroom to run it comfortably:
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See how Phi-4 Mini stacks up against other models on your specific Mac hardware.
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