Gaming benchmarks last minutes. Local LLM inference can pin a GPU for hours. Mechanical and thermal details that never showed up in a short game test start to matter.
Why long sessions differ
Sustained load keeps vapor chambers and heatsinks in a hot steady state. A mediocre mount or dried paste that worked in winter gaming may throttle during a summer afternoon of batch summarization. CPU paste similarly matters if you offload layers.
DIY checks
- Reseat the card and confirm power connectors are fully clicked.
- Monitor temperatures across a one-hour realistic job, not a fifteen-second demo.
- Improve case airflow before jumping to exotic cooling.
- Replace aging paste on older CPUs when temperatures creep year over year.
- Keep dust filters clean; choking intake raises fan noise and heat together.
Build planning
Choose cases with a clear front-to-back path, especially for multi-slot cards. Budget builds still need this discipline: see budget 3000, budget 5000, and the general hardware guide. Lab-oriented notes sit at lab. Matching model size so you are not forcing absurd clocks to compensate is easier with the match pages.
Operational habit
Log a monthly temperature sample under a fixed prompt load. Sudden jumps after a move or cable change deserve a physical look. Cooling will not add VRAM, but it keeps the VRAM you bought from spending the day thermal throttling in silence.
Extended cooling checklist
After any chassis move, re-check fan headers and whether the GPU sag bracket still holds alignment. Sag can worsen over months and change thermal contact subtly. If you use an undervolt profile, re-validate stability after paste changes. Keep a spare set of quality case fans; failed cheap fans are a common path to emergency throttling during demos. Thermal work is unglamorous and compounds with dust management into the difference between a machine people trust and a machine people unplug. Write the healthy temperature band next to the model name in your runbook so on-call staff know what normal looks like.
Published by MicroAI One (本地AI 1号网), a Shenzhen team working on local LLM hardware, software and deployment.