When running local AI workloads and large language models (LLMs), raw compute (FLOPS) is often secondary to unified memory bandwidth and capacity.

Without sufficient VRAM or unified memory, running 70B+ parameter models at full precision becomes practically impossible without severe quantization, complex multi-GPU setups, or cloud dependencies.

I recently read an excellent analysis by Kunal Ganglani on how Apple’s M5 Max chip architecture impacts local AI execution:

🔗 Reading: Apple’s M5 Max Just Made the Case for Local AI Development

Key Takeaways for AI Engineers:

• The Memory Wall: Token generation in local LLM inference is strictly memory-bandwidth bound.
• Unified Memory vs. VRAM: Having up to 128GB of unified memory at 614 GB/s enables loading massive models locally without splitting across multiple GPUs or taking severe quality hits via quantization.
• Frictionless Prototyping: Compress the gap between testing a model locally and deploying agentic workflows without cloud costs or DevOps overhead.

A must-read breakdown if you are designing local fallback environments for AI agents.