Optimizing TTS autoscaling on Amazon SageMaker without guesswork

Amazon SageMaker AI introduces a method to avoid guesswork in setting autoscaling targets for text-to-speech (TTS) models. Teams deploying custom TTS models often rely on manual thresholds, leading to wasted GPU spend or latency SLO breaches.

The post details the deployment of Kokoro-82M, an 82-million-parameter TTS model, in a custom container on SageMaker. Two key measurements, Q_max and T_total, replace manual estimates to define an accurate autoscaling policy. Q_max is the highest concurrency one instance can handle without breaching the latency SLO, while T_total measures the time required for a full scale-out. These values are derived from real-world tests and integrated into the configuration using AWS CDK stacks.

This approach eliminates risks tied to manual estimates and enables automatic adaptation to traffic changes. The provided tools, including a CLI command chain, ensure autoscaling policies are only deployed after validation by concrete measurements, preventing untested configurations from reaching production.