Burst GPU demand keeps cloud rentals relevant

This is the practical infrastructure problem behind local AI work: bursty demand breaks the economics of owning everything yourself. When bigger runs only show up a few times a month, flexible GPU access and job-style workflows make more sense than idle hardware.

• –The post captures a common local-LLM pattern: local inference is cheap day to day, but experiments and batch jobs quickly hit VRAM and throughput limits.
• –Community responses point toward on-demand services like Salad-style GPU credits rather than full-time server management.
• –The real bottleneck is often operational, not just compute price; simple job submission matters more than raw access to another machine.
• –For AI developers, this reinforces that hybrid local-plus-cloud setups are becoming the default operating model.