Apple Silicon for Local LLMs
🍎Apple Silicon architecture, particularly the M4 Pro, offers a more cost-effective solution for running local Large Language Models compared to expensive Nvidia GPUs, with performance similar to two RTX 490s at a fraction of the price.
🚀Running a 7 billion parameter model on a single M4 Pro achieves 12 tokens per second, while two M4 base models manage 8 tokens per second, demonstrating the M4 Pro's significant speed advantage for certain machine learning tasks.
GPU vs CPU for Machine Learning
🖥️GPUs excel at parallel processing, making them ideal for machine learning models, while CPUs struggle with these tasks due to their sequential processing nature.
Cluster Computing for ML
🔗EXO, a distributed computing framework, simplifies cluster setup for machine learning but introduces some performance overhead compared to direct model execution.
Hardware Considerations
💾For certain machine learning models, the amount of RAM (e.g., 24GB in M4 Pro vs 16GB in M4) doesn't significantly impact performance, challenging common assumptions about hardware requirements.