Running Local LLMs Offline on a Ten-Hour Flight (deploy.live)

During a recent ten-hour flight from London to Las Vegas, an engineer tested the capabilities of local large language models (LLMs) on a high-performance MacBook Pro M5 Max. Using models like Gemma 4 31B and Qwen 4.6 36B, he managed to develop a robust billing analytics tool while managing power and thermal constraints. The experience highlighted both the potential of running LLMs offline for specific tasks and the practical challenges encountered, such as battery drain under sustained load, heat management issues, and limitations in context handling beyond 100k tokens. This exploration is significant for the AI/ML community as it demonstrates the viability of local inference for targeted engineering workloads, suggesting that tasks which don't warrant cloud computing could be effectively handled on personal machines. Key findings included the importance of accurately monitoring power usage and minimizing performance overhead, as well as the need for innovative solutions to mitigate heat and battery challenges. Ultimately, the engineer’s experience reinforces the value of local processing, prompting a more disciplined approach to resource management that could enrich cloud-based AI workflows in the future.