llms local-ai developer-tools offline-computing

Running local LLMs offline on a ten-hour flight

deploy.live

April 27, 2026

4 min read

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53/100

Summary

A MacBook Pro M5 Max with 128GB of unified memory and a 40-core GPU was used to run local LLMs offline during a ten-hour flight. Gemma 4 31B and Qwen 4.6 36B were tested alongside the top 100 most common Docker images and programming languages, enabling the building of function sites with rich visualizations.

Key Takeaways

A MacBook Pro M5 Max was used to run local LLMs offline during a ten-hour flight, achieving comparable output to frontier models for various engineering tasks.
The author developed a billing analytics tool using DuckDB and custom UI, revealing patterns in cloud spending that standard dashboards did not expose.
Significant limitations encountered included power consumption of 1% battery per minute under load, overheating issues, and degraded performance beyond 100k tokens.
The author created two monitoring tools, powermonitor and lmstats, to track power usage and model performance during the flight.

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Analysis ready

Community Sentiment

Mixed

Positives

Qwen3.6 27B is being recognized as a consumer-grade model capable of replacing advanced workloads, indicating significant progress in local AI model capabilities.
Local models can be tailored for specific tasks, providing users with the flexibility to optimize performance according to their needs, which is a step towards greater accessibility.

Concerns

Many users find that local models often lead to unproductive infinite reasoning loops, suggesting that current implementations may not be robust enough for meaningful applications.
There is skepticism about the hype surrounding local AI models, with some users feeling that they fail to deliver substantial results compared to hosted solutions.