Local / On-Prem Models: Easy Installation & Trial Runs

[Juice10]

What: Make it simple for users to install, configure, and experiment with AI models on their own machines or servers—no complex setup required.
Why: Speed up responses by running close to home, maintain full control over data for security/privacy, and allow offline usage for teams in restricted environments.
Focus: Streamlined installation (e.g., one-click installers, Docker images, setup scripts) and well-documented instructions for quickly trying out the model on a local or on-prem setup.
Questions:
What installation paths or tools (Docker, Conda, etc.) would make setup easiest for the widest range of users?
How do we handle updates and patches—do we auto-update or provide version pinning for stability?
Are there any recommended hardware specs or resource constraints we should make clear from the start?
How do we keep installation secure and well-tested across different operating systems?
Should we bundle pretrained models or offer a “bring your own model” approach?
We’d love to hear any ideas on how to make local/on-prem model trials as painless as possible. Feedback, experiences, and suggestions welcome!