How Deepgram optimized voice AI at scale with AWS Trainium
TL;DR
Deepgram worked with the AWS Forward Deployed Engineering team to port its Nova-3 speech-to-text model to AWS Trainium2 for offline batch inference. This post covers porting to the AWS Neuron stack, kernel-level tuning, a data-parallel Rust runtime, and the throughput and cost results on batch transcription.
Nauti's Take
The practical test is for predictable transcription pipelines: run the same batch on Trainium2 and a GPU using real audio, measuring latency, error rates, and total cost. Before migrating, teams should verify the engineering effort required for Neuron and whether their workload gains enough from offline processing to justify a dedicated runtime.
Sources
16.10.26
