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AWS details a measurement-driven auto scaling method for text-to-speech AI endpoints on SageMaker
Learn how to replace guessed auto scaling targets for real-time text-to-speech AI models on Amazon SageMaker AI with live endpoint measurements.
Summary
Amazon Web Services released a workflow for setting precise auto scaling targets for text-to-speech AI endpoints on Amazon SageMaker AI. Instead of guessing scaling thresholds, operators measure Q_max (the concurrency ceiling before breaching the SLO) and T_total (the total scale-out time) on a live endpoint. These two measurements are then used to calculate scale-out thresholds, scale-in thresholds, and cooldown durations via arithmetic.
What changed
- Measured Q_max concurrency ceiling for Kokoro-82M AI model running on Amazon SageMaker AI.
- Measured T_total scale-out time for Kokoro-82M AI endpoints.
- Derived arithmetic auto scaling thresholds and asymmetric cooldowns from live endpoint measurements.
Availability
Available now“Every number in this post comes from an actual deployment and measurement run of the aws-samples repository referenced in this post.”
Founder takeaway
If you run latency-sensitive text-to-speech or concurrency-bound AI models on SageMaker, replace default throughput metrics with direct concurrency measurements to prevent silent SLO breaches or idle GPU waste.
Source
Researched and drafted with AI from the official sources above, then checked automatically before publishing. Spotted a mistake? Tell us.
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