Deepgram Boosts Speech‑to‑Text Efficiency 11‑70% Using AWS Trainium2

Deepgram ported its Nova‑3 speech‑to‑text model to AWS Trainium2, deploying eight replicas on a single trn2.3xlarge EC2 instance. The optimized configuration processed 11–70 % more audio per dollar on batch transcription workloads compared to previous compute options. The team used TorchNeuron for native PyTorch integration, performed kernel‑level tuning, and built a Rust‑based data‑parallel runtime to eliminate Python overhead. These gains enable Deepgram to serve more audio for the same infrastructure budget and lower costs for customers who self‑host on SageMaker AI.