When does autoscaling GPUs for inference cost more than keeping them always on?
Mobile-Lab-4586 · reddit · 2026-10-29
A Reddit discussion on LLM inference cost trade-offs: autoscaling GPUs down with traffic seems to save money, but inference load is hard to predict — bursty traffic, varying latency needs, and cold-start delays as new workers boot and load model weights.
Keeping GPUs always-on avoids these issues but wastes idle capacity. AWS, Google Cloud, CoreWeave and Yotta Labs offer various scaling options, yet the right choice depends on the workload. The author asks practitioners running production LLM inference how they choose between always-on GPUs, autoscaled workers, and serverless inference.
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