This research evaluates Apple Silicon's performance for machine learning training compared to traditional NVIDIA GPUs, with special focus on the Unified Memory architecture.

• Investigates whether Apple's integrated CPU-GPU memory approach offers performance benefits over NVIDIA's separate VRAM design
• Analyzes hardware engineering aspects of Apple Silicon's unique architecture
• Provides quantitative performance insights for ML training workloads
• Helps engineering teams make informed decisions about hardware selection for ML projects

Engineering implications: This work offers critical data for organizations evaluating hardware investments, particularly those considering Apple Silicon for machine learning infrastructure.

Profiling Apple Silicon Performance for ML Training