Deployment
Local LLM
A local LLM runs on your own machine instead of a cloud API.
Quick definition
A local LLM runs on your own machine instead of a cloud API.
- Category: Deployment
- Focus: hosting and runtime tradeoffs
- Used in: Running local models for privacy-sensitive workflows.
What it means
It improves privacy and can reduce recurring costs. In deployment workflows, local llm often shapes hosting and runtime tradeoffs.
How it works
Deployment choices include cloud APIs, local inference, or hybrid setups. Each option trades off privacy, cost, and performance.
Why it matters
Deployment choices affect privacy, performance, and cost.
Common use cases
- Running local models for privacy-sensitive workflows.
- Using managed APIs for fast iteration and scaling.
- Hybrid setups that keep data local but call cloud models.
Example
Run a model with a local inference server.
Pitfalls and tips
Local deployments require hardware planning and updates. Cloud deployments require governance and cost control.
In BoltAI
In BoltAI, this appears in provider, hosting, or local model settings.
