# Port isolation for multiple backends

Teams that run local LLMs usually stall on habits, not on model names: messy folders, unclear versions, and no acceptance checks. This note turns 「Port isolation for multiple backends」 into a short procedure a small team can follow.

# Why write it down

Verbal rules disappear after a rotation. A short page with links to verified sections on the site beats searching chat history. Start with install; for hardware or build context open home ai server nas.

# Steps

1. Define done: what evidence counts and who signs off.
2. Change one variable at a time—weights, quantization, or concurrency.
3. Keep a minimal record: date, host name, command or UI path, and a log snippet.
4. Reproduce failures before patching; avoid stacking fixes on unreproducible states.

# Common drifts

• Copying demo configs into production without checking permissions and networks;
• Chasing larger models while ignoring disk, PSU and cooling headroom;
• Skipping a public “what we will not do” note, which invites over-trust.

# Close

Sync the conclusion to your internal doc and validate with two real examples in the next standup. Steady local AI comes from repeatable small steps. Browse the site sections when you need deeper tutorials or build notes.

Published by MicroAI One (本地AI 1号网), a Shenzhen team working on local LLM hardware, software and deployment.