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Apple Silicon for Local AI: M4, M4 Pro, M4 Max Compared (2026)

Complete guide to running AI models on Apple Silicon. Compare M4, M4 Pro, and M4 Max for local LLM inference with MLX and llama.cpp benchmarks.

Last updated: February 7, 2026

🎯 Why This Matters

Apple Silicon's unified memory architecture is a game-changer for local AI. Unlike discrete GPUs where you're limited by VRAM, Apple's M-series chips share all system memory between CPU and GPU. A Mac with 64GB unified memory can run models that would need a $1,600 GPU on PC. Plus, Macs are silent, energy efficient, and the MLX framework is optimized specifically for Apple hardware.

🏆 Our Recommendations

Tested and ranked by real-world AI performance

💚 Budget

Mac Mini M4 (24GB)

$699
VRAM24 GB unified
SpecsM4 chip, 10-core CPU, 10-core GPU, 24GB unified memory, 100 GB/s bandwidth
Performance~18 tok/s with 7B Q4, ~8 tok/s with 13B Q4
Best For7B-13B models, silent home AI, macOS users

✅ Pros

  • $699 for a complete system
  • 24GB unified memory
  • Dead silent
  • 5W idle power draw
  • Great as always-on AI server

❌ Cons

  • Slower than NVIDIA GPU inference
  • 24GB limits model size
  • Non-upgradeable RAM
  • 100 GB/s bandwidth is limiting
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💙 Mid-Range

Mac Mini M4 Pro (48GB)

$1,599
VRAM48 GB unified
SpecsM4 Pro, 14-core CPU, 20-core GPU, 48GB unified memory, 273 GB/s bandwidth
Performance~28 tok/s with 7B Q4, ~14 tok/s with 13B Q4, ~6 tok/s with 30B Q4
Best For13B-30B models, serious local AI, developers

✅ Pros

  • 48GB runs 30B models
  • 273 GB/s bandwidth — 2.7x faster than M4
  • Excellent thermal performance
  • Thunderbolt 5
  • Still very quiet

❌ Cons

  • $1,599 is significant
  • 30B inference still slower than RTX 4090
  • Can't upgrade memory
  • M4 Pro GPU not as fast as discrete
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💜 High-End

Mac Studio M4 Max (128GB)

$3,499
VRAM128 GB unified
SpecsM4 Max, 16-core CPU, 40-core GPU, 128GB unified memory, 546 GB/s bandwidth
Performance~40 tok/s with 7B, ~22 tok/s with 13B, ~12 tok/s with 70B Q4
Best For70B models, maximum Apple performance, professional use

✅ Pros

  • 128GB runs 70B models on a single machine
  • 546 GB/s bandwidth
  • Silent under load
  • Energy efficient (~100W)
  • Best macOS AI experience

❌ Cons

  • $3,499 is expensive
  • Slower than dual RTX 4090 for 70B
  • Apple ecosystem lock-in
  • Non-upgradeable
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💡 Prices may vary. Links may earn us a commission at no extra cost to you. We only recommend products we'd actually use.

🤖 Compatible Models

Models you can run with this hardware

DeepSeek R1 14B

14B

10 GB min VRAM · DeepSeek

DeepSeek R1 32B

32B

20 GB min VRAM · DeepSeek

DeepSeek R1 7B

7B

6 GB min VRAM · DeepSeek

Gemma 2 9B

9B

7 GB min VRAM · Google

Mistral 7B

7B

6 GB min VRAM · Mistral AI

Llama 3.3 70B

70B

40 GB min VRAM · Meta

Phi-4

14B

10 GB min VRAM · Microsoft

Qwen 2.5 7B

7B

6 GB min VRAM · Alibaba

❓ Frequently Asked Questions

Is Apple Silicon good for AI?

Yes, especially for its price-to-memory ratio. A $699 Mac Mini with 24GB unified memory matches the memory capacity of a $1,600 RTX 4090 — though NVIDIA is faster per-token. Apple's strength is silent operation, efficiency, and the ability to get massive memory (128GB) in a compact system.

MLX vs llama.cpp on Mac — which is faster?

MLX is Apple's native framework and is ~10-20% faster than llama.cpp on Apple Silicon for most models. Use MLX via tools like LM Studio or the mlx-community models on Hugging Face. llama.cpp works great too and is more portable.

Should I get a Mac or PC for local AI?

PC with NVIDIA GPU if you want maximum speed and flexibility. Mac if you want silence, efficiency, large memory (64-128GB), and a clean setup. Mac is especially compelling for 70B models where unified memory eliminates multi-GPU complexity.

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