Run Phi-4 14B on M2

Yes — Phi-4 14B (14B) runs at 28 tok/s on M2 with 16 GB RAM using Q4_K_M quantization via MLX. First token latency is 1.3s. Microsoft's 14B Phi-4 model with strong math and coding performance.

Speed

tok/s

First Token

1.3

seconds

RAM Needed

GB minimum

Engine

MLX

recommended

Benchmark Details

LLMCheck measured Phi-4 14B on M2 using the standard methodology: Q4_K_M quantization, 256-token input, 512-token output, 3 runs averaged on a freshly-booted system.

Metric	Value
Tokens per second	28 tok/s
Time to first token	1.3s
Quantization	Q4_K_M
Minimum RAM	16 GB
Recommended engine	MLX
Parameters	14B
Benchmark date	2026-01

Q4_K_M 14B MLX M2

Setup Guide: Run Phi-4 14B on M2

The recommended engine for Phi-4 14B on M2 is MLX. Install with pip and pull the model:

pip install mlx-lm

mlx_lm.generate --model mlx-community/phi-4-14b-q4_k_m --prompt "Hello!"

Alternatively, you can use Ollama for a simpler setup:

ollama run phi4:14b

Performance on Other Apple Silicon Chips

Chip	Speed	First Token	Min RAM	Engine
M5 Max	62 tok/s	0.6s	64 GB	MLX
M4	38 tok/s	1.0s	16 GB	Ollama

System Requirements

To run Phi-4 14B on M2 you need:

• Mac with M2 chip (or newer)
• 16 GB unified memory minimum
• macOS 13 Ventura or later
• ~13-16 GB free disk space for the model file
• MLX installed (see our Ollama install guide)

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