Local LLMs, API models, and self-hosting

🟡 GOOD TO KNOW · 🟢 ADVANCED for high-throughput serving and GPU optimization.

Overview

• Local or self-hosted models run on infrastructure you control instead of a provider's shared inference API.
• An API model outsources much of inference operation.
• Self-hosting can mean a laptop, on-premises servers, or controlled cloud GPU infrastructure.

Why this matters

Some organizations need approved offline operation, specific deployment control, or economics suited to sustained utilization.

An everyday example

Run your own kitchen instead of ordering prepared meals; you gain control but must manage equipment and staffing.

A technical example

Deploy a permitted model behind an internal authenticated endpoint and benchmark it on your claim-extraction dataset.

How it works

1. Select licensed model
2. Choose runtime/hardware
3. Load weights
4. Expose secured inference
5. Manage capacity and versions
6. Evaluate and monitor.

Architecture and flow

Node AI adapter → private inference endpoint → model runtime/GPU → validated response.

The insurance assistant example

A restricted document-processing workload may run inside the insurer's controlled environment if quality and operating capacity meet requirements.

When to use it

Explicit deployment restrictions, offline use, or measured total-cost benefit with an operations team.

When to avoid it

Low traffic, insufficient hardware expertise, or quality gaps often favor an approved hosted provider.

Interview answer

“Self-hosting trades provider convenience for deployment control and operational responsibility. I compare quality and total cost at realistic utilization.”

Benefits, trade-offs, and production considerations

• Local does not automatically mean private: logs, backups, downloads, remote access, and exposed ports still matter.
• Check model licenses, patch runtime dependencies, and budget idle capacity, redundancy, and staff time.