Local-LLM Orchestration

Running capable AI locally — for privacy, for cost, for not shipping your data to someone else — sounds simple until you try it. The good models are big, several of them won't fit in memory at once, and the hardware has hard limits. Naively loading what you need, when you need it, gets you crashes instead of answers.

Treat the machine like a small model-serving setup, not a laptop running a script:

• A few specialized models, each behind a stable endpoint
• The right model kept warm for the job that needs it — swapped by workload and time of day
• Memory managed deliberately, so a heavy job doesn't take everything else down with it

It's deliberately boring infrastructure. That's the point — boring is what makes "local AI" reliable instead of a once-it-worked demo.

This is the unglamorous layer that makes private, local AI actually dependable — the difference between "I ran a model once" and "this runs every night and I trust it."

For a team weighing local vs. cloud, the real question is never can you run it locally — it's whether you can run it reliably. That's a serving-and-orchestration problem, and it's a solvable one.