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Amazon Web Services released TorchNeuron, a PyTorch-compatible tool that AWS says lets developers run PyTorch apps on its Trainium AI chips with minimal configuration (one-line change). AWS also plans to contribute upstream changes to PyTorch to reduce framework tie‑ins to specific chips and argues the move gives startups easier access to AWS cloud and better price‑performance across training and inference.
Deepgram worked with AWS to port its Nova-3 speech-to-text model to AWS Trainium2, using TorchNeuron, NKILib kernels and custom Neuron kernels; the final setup runs eight model replicas on a single Amazon EC2 trn2.3xlarge instance. On Deepgram’s offline, throughput-oriented batch workloads (e.g., contact-center analytics, medical transcription) this configuration processed 11–70% more audio per dollar versus the compute options Deepgram previously used, measured using instance pricing at the time of evaluation.
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