Deep Dive: Autoscaling Strategies for Peaky LLM Inference Workloads
zainhas · x · 2026-08-08
Autoscaling peaky LLM inference workloads differs fundamentally from traditional web services. Together AI published a deep dive exploring autoscaling strategies for dedicated inference endpoints.
The article highlights that over-provisioning wastes GPU resources, while under-provisioning causes non-linear latency degradation under load. It details how to choose the right scaling metrics—such as in-flight requests, TTFT, GPU utilization, and token throughput—and how to tune scale-up and scale-down windows. An experiment comparing three different autoscale policies under the same replayed load is also included.
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