Understand this story
Deepgram and AWS collaborated to port the Nova-3 speech-to-text model to AWS Trainium2. According to information published by AWS Machine Learning on October 20, 2026, a single trn2.3xlarge instance can run 8 model replicas, processing 11%–70% more audio per dollar in offline batch processing scenarios than the previous solution. The team used TorchNeuron to complete the PyTorch port and tuned it through NKILib fused kernels and hand-written NKI kernels, among which the FFN kernel latency was reduced by 2.0x, depthwise conv1d was sped up by 1.5x, and the conv2d kernel unlocked batch sizes above 32. There are currently no subsequent development facts.
AI-generated from public reports
Latest development · Report
How Deepgram optimized large-scale speech AI with AWS TrainiumDevelopments
1 confirmed developments
- Source:AWS Machine Learning
Reports and discussion timeline
Browse reports and discussion by time. Their evidence levels remain distinct.
Oct 20
AWS Machine LearningFirst-party
How Deepgram optimized large-scale speech AI with AWS Trainium
Deepgram partnered with AWS to port the Nova-3 speech-to-text model to Trainium2, running 8 model replicas on a single trn2.3xlarge instance, achieving an 11%–70% increase in audio processed per dollar compared with the previous solution in offline batch processing scenarios. The team used TorchNeuron to complete the PyTorch port and tuned it through NKILib fused kernels and hand-written NKI kernels, among which the FFN kernel reduced latency by 2.0x, depthwise conv1d sped up by 1.5x, and the conv2d kernel unlocked batch sizes above 32.