DevPulse → Can I Run It? → Phi-4 14B

Can I Run

Phi-4 14B

Microsoft's flagship reasoning model. Exceptional quality for a 14B model, especially on STEM tasks.

ChatCodeReasoning
14B
Parameters
16K
Context
8.7 GB
RAM (Q4_K_M)

RAM by quantization

Lower quantization = less RAM but lower quality. Q4_K_M is the recommended sweet spot for most users.

FormatBitsRAMQualityVerdict
Q3_K_M37.0 GBModerateRuns great
Q4_K_MREC48.7 GBGoodRuns great
Q5_K_M510.4 GBGoodRuns great
Q6_K611.6 GBExcellentRuns great
Q8_0815.0 GBExcellentRuns OK
F161628.6 GBLosslessAfter cleanup

Which Mac can run Phi-4 14B?

Based on the recommended Q4_K_M quantization. You need RAM for both the model and your running apps — DevPulse calculates this for you. No CUDA installation. No driver hell. Just Apple Silicon doing what Jensen charges $30K for.

8 GB
Can’t run
16 GB
Close apps first
~7 GB for apps
24 GB
Runs well
~15 GB for apps
32 GB
Runs great
~23 GB for apps
36 GB
Runs great
~27 GB for apps
48 GB
Runs great
~39 GB for apps
64 GB
Runs great
~55 GB for apps
96 GB
Runs great
~87 GB for apps
128 GB
Runs great
~119 GB for apps
192 GB
Runs great
~183 GB for apps

Tips for running Phi-4 14B

1 Q4_K_M at 8.7 GB needs a 16 GB Mac — close heavy apps first

2 MIT license makes this ideal for commercial code generation

3 Use DevPulse to free up memory before loading — Docker alone might free 4 GB

4 Shorter 16K context — best for focused tasks, not long documents

Skip the cloud GPU bill

Why pay $3/hr for an A100 when your Mac has unified memory sitting idle? DevPulse tells you if Phi-4 14B fits right now — factoring in Chrome, Docker, and everything else hogging your RAM.

Download Free

Model details

Provider
Microsoft
Architecture
dense
License
MIT
Commercial use
Yes
Min RAM
8 GB
Recommended
12 GB
Released
2025-01
Ollama
phi4
VRAM estimates from CanIRun.ai. Data sourced from llama.cpp, Ollama, and LM Studio.

Tokens per second on Apple Silicon

How fast will Phi-4 14B run on each chip?

Apple Silicon inference is bandwidth-bound — every generated token streams the model's active weights through unified memory once. Estimates are for single-batch generation at Q4_K_M (8.7 GB) at ~70% of peak bandwidth (typical llama.cpp / Ollama efficiency). Speculative decoding can lift these another 30-60%.

ChipBandwidthSmallest RAM that fitstok/s (est.)
M168 GB/s16 GB~5 tok/s
M2100 GB/s16 GB~8 tok/s
M3100 GB/s16 GB~8 tok/s
M4120 GB/s16 GB~10 tok/s
M2 Pro200 GB/s16 GB~16 tok/s
M3 Pro150 GB/s18 GB~12 tok/s
M4 Pro273 GB/s24 GB~22 tok/s
M2 Max400 GB/s32 GB~32 tok/s
M3 Max400 GB/s36 GB~32 tok/s
M4 Max546 GB/s36 GB~44 tok/s
M2 Ultra800 GB/s64 GB~64 tok/s
M3 Ultra819 GB/s96 GB~66 tok/s

“Smallest RAM that fits” assumes ~40% headroom for context, OS, and your dev stack. Reclaim VRAM before loading →

Running it offline

Local-AI guides for Phi-4 14B.

Knowing the model fits is half the problem. The other half is keeping your Mac's unified memory free enough to actually load it, and keeping the load alive across a long session.

CLI for local AI agents →

Pre-flight checks, JSON output, exit codes for scripts. Built for Ollama, llama.cpp, LM Studio, and MLX.

Free VRAM before loading →

The 6-step protocol for clearing unified-memory headroom on Apple Silicon, from cheapest to deepest.

Fix Ollama OOM on Mac →

When Ollama says “out of memory” on a Mac with plenty of RAM. Diagnose and fix in one command.

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Run Phi-4 14B locally. No GPU required.

While cloud GPU prices keep climbing, your Mac can run Phi-4 14B for free. DevPulse tells you if it fits alongside your dev tools — before you download 8.7 GB of model weights.

Download for macOS

macOS 14+ · Apple Silicon & Intel · Free during launch