MacBook Pro Max fuels local LLMs

The real question here is not whether the machine is powerful enough, it is which workloads justify burning 128GB of unified memory. This is a strong local-AI workstation, but the ceiling will come from token throughput, model choice, and tooling discipline more than raw RAM.

• –Local inference stacks like Ollama and LM Studio are the obvious on-ramp for trying models quickly
• –Mid-size models for chat and coding are the sweet spot before diminishing returns hit on latency
• –Agentic workflows can use the hardware well, but they need careful orchestration to avoid wasting cycles
• –Benchmarking token/sec and context length matters more than chasing the biggest parameter count
• –Apple silicon is best treated as a portable personal AI lab, not a drop-in replacement for a GPU server