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RUNLOCALAI · v38
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  6. /Ch. 8
Hardware Planning for Local AI

08. Apple Silicon Deep Dive

Chapter 8 of 20 · 15 min
KEY INSIGHT

Unified memory bandwidth directly limits AI performance—prioritize memory capacity over GPU core count for local AI workloads. ```bash # macOS: Check Apple Silicon specs system_profiler SPHardwareDataType # Verify MLX installation for optimized inference pip3 install mlx python3 -c "import mlx.core; print(mlx.core.default_device())" # Speed test with MLX python3 -c " from transformers import AutoModelForCausalLM import time # Requires hummingbird or local model " ```

Apple Silicon provides compelling performance for local AI through tight hardware-software integration. Understanding the unified memory architecture is essential.

Current Apple Silicon lineup

Chip CPU Cores GPU Cores Unified Memory Neural Engine
M2 8 (4P+4E) 10 Up to 24GB 16-core
M2 Pro 10+2 (P+E) 16-19 Up to 32GB 16-core
M3 8 (4P+4E) 10 Up to 24GB 16-core
M3 Pro 11+5 (P+E) 14-18 Up to 36GB 16-core
M3 Max 12+4 (P+E) 30-40 Up to 128GB 16-core
M4 8 (4P+4E) 10 Up to 24GB 38-core
M4 Pro 10+4 (P+E) 20 Up to 64GB 38-core
M4 Max 12+4 (P+E) 32-40 Up to 128GB 38-core

Unified Memory Architecture

Unlike NVIDIA systems where VRAM is separate from system RAM, Apple Silicon shares memory between CPU, GPU, and Neural Engine. Bandwidth scales with memory size:

Memory Config CPU→Memory BW GPU→Memory BW
24GB 100 GB/s 300 GB/s
36GB 150 GB/s 400 GB/s
64GB 200 GB/s 500 GB/s
128GB 300 GB/s 800 GB/s

The M3 Max 128GB configuration matches data center GPU memory bandwidth while using unified architecture.

Performance Benchmarks

Running Llama 3 8B via llama.cpp with Metal backend:

Device Backend Tokens/sec
M2 MacBook Air 24GB Metal 18-22
M3 Pro MacBook Pro 36GB Metal 35-40
M3 Max MacBook Pro 128GB Metal 75-85
M4 Max MacBook Pro 128GB Metal 95-110

GPU Cores and AI Workloads

GPU core count affects inference performance differently than raw compute:

  • 7B INT4 models: 14-16 GB requirement
  • 13B INT4 models: 22-26 GB requirement
  • Full performance requires 24GB+ unified memory

M2 (10-core GPU) is constrained for larger models. M3 Pro and above provide better headroom.

Power Efficiency

Running Mistral 7B on battery:

  • M2 MacBook Air: 8W average, 4-6 hours
  • M3 MacBook Pro 14": 12W average, 8-10 hours

Equivalent NVIDIA laptop would require 50W+ for similar performance.

EXERCISE

Compare the cost-per-GB of unified memory across M3 Pro (36GB), M3 Max (64GB), and M3 Max (128GB) configurations. Calculate which provides best value for running 13B models.

← Chapter 7
AMD ROCm Compatibility
Chapter 9 →
System RAM Benefits