AI × AEC · CURATED INSIGHT
Empirical Auto-Scaling Sizing for SageMaker TTS Models
AWS published a benchmarking method for text-to-speech models like Kokoro-82M on Amazon SageMaker AI, deriving auto-scaling targets from live endpoint concurrency and scale-out timing instead of guessed metric targets.
AWS detailed an empirical auto-scaling methodology for text-to-speech models hosted on Amazon SageMaker AI, demonstrating with Kokoro-82M. Rather than guessing scaling thresholds, the approach measures per-instance concurrency limits and scale-out duration to avoid queueing delays and service-level objective breaches during traffic surges.
Key takeaways
- AWS notes that text-to-speech workloads lack token-generation loops, making SageMaker's native ConcurrentRequestsPerModel metric more appropriate for auto scaling than per-token throughput.
- Load testing Kokoro-82M identified a concurrency ceiling (Q_max) of 50 requests before exceeding a 3,000-millisecond time-to-first-audio-byte service-level objective at concurrency 60.
Source：AWS Machine Learning Read the original ↗
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