Phi-4 on Apple Silicon

Microsoft's 14B reasoning model. Fits comfortably on a 24 GB Mac. Strong performance per watt and per RAM-GB.

Phi-4 is a 14B parameter model from Microsoft Research, released in late 2024. It focuses on reasoning quality through high-quality synthetic training data. Phi-4 consistently outperforms models significantly larger than it on reasoning benchmarks — making it an excellent choice for developers who want strong reasoning without the RAM overhead of 32B+ models.

~9 GB RAM at Q4_K_M

~15 GB RAM at Q8_0

24 GB+ recommended Mac (M Pro+)

14B parameters with 16K context

Estimated inference speed by chip

Phi-4 is a 14B dense transformer. Speed should be close to Qwen 2.5 14B Instruct, which has measured data in the SiliconBench dataset. The estimates below use those measurements as a proxy.

SiliconBench does not yet have first-party Phi-4 benchmark data. Estimates are based on Qwen 2.5 14B Instruct measurements, which uses a similar architecture and parameter count.

Chip	RAM	Qwen 2.5 14B (measured)	Phi-4 (estimated)	Source
M4 Max (40-core GPU, 64 GB)	64 GB	30.1 tok/s	~28–32 tok/s	estimated
M4 Pro (20-core GPU, 48 GB)	48 GB	18.0 tok/s	~17–20 tok/s	estimated
M4 Pro (16-core GPU, 24 GB)	24 GB	15.2 tok/s	~14–17 tok/s	estimated
M3 Max (40-core GPU, 128 GB)	128 GB	25.5 tok/s	~24–28 tok/s	estimated
M3 Max (30-core GPU, 36 GB)	36 GB	19.8 tok/s	~18–22 tok/s	estimated
M3 Pro (18-core GPU, 36 GB)	36 GB	12.1 tok/s	~11–14 tok/s	estimated
M2 Ultra (76-core GPU, 128 GB)	128 GB	36.6 tok/s	~34–40 tok/s	estimated

See full Qwen 2.5 14B Instruct benchmark data → (proxy data for Phi-4 estimates)

Phi-4 vs Qwen 2.5 14B and Llama 3.1 8B

Phi-4's key advantage is reasoning quality per parameter. It outperforms 7B and 8B models significantly on reasoning tasks despite similar inference speed to 14B models.

Model	Params	RAM at Q4_K_M	Speed on M4 Pro 24 GB	Reasoning quality
Phi-4	14B	~9 GB	~14–17 tok/s	Excellent for size
Qwen 2.5 14B Instruct	14B	~9 GB	15 tok/s	Very good
Llama 3.1 8B Instruct	8B	~4.7 GB	32 tok/s	Good

Phi-4 is the best 14B reasoning model for local use on Macs with 24 GB or more.

At similar RAM requirements and inference speed to Qwen 2.5 14B, Phi-4 delivers notably stronger reasoning capabilities — particularly on math, coding, and multi-step problems. For developers who primarily use local LLMs for analytical tasks, code review, or research, Phi-4 is a strong choice. For conversational chat and summarization, Qwen 2.5 14B and Llama 3.1 8B are competitive and faster (8B is ~2× faster than 14B).

Running Phi-4 with Ollama

# Run Phi-4 (14B, auto-selects Q4_K_M ~9 GB)
ollama run phi4

# Run Phi-4 at Q8_0 for maximum quality (~15 GB)
ollama run phi4:q8_0

# Run Phi-4 Mini (smaller, faster variant)
ollama run phi4-mini

Related model and hardware pages

Qwen 2.5 14B benchmarks Llama 3.1 8B benchmarks DeepSeek R1 on Apple Silicon M4 Max vs M4 Pro RAM Calculator Best Mac for local LLMs

Data

benchmarks.json — full dataset · chips.json — chip summaries · benchmarks.csv — CSV export

See all chips →