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AWS Trainium2

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2

MODELS · 1 SOURCE · AWS Machine Learning

i14 trains and serves Laplace 3B linear-attention model on AWS Trainium2 with TorchNeuron Native

i14 ported its Laplace 3B 3B-parameter linear-attention decoder (using Gated Linear Attention and a fixed 29.5 MB recurrent state) to AWS Trainium2 via TorchNeuron Native and native PyTorch operators. On a trn2.48xlarge instance the team reported ~120,000 training tokens/sec and serving throughput of 11,544 output tokens/sec across 1,280 concurrent sequences after replacing a Triton kernel, optimizing MuonFSDP2 collectives, and statically unrolling the GLA recurrence for the Trainium2 compiler.

7.0

MODELS · 1 SOURCE · AWS Machine Learning

Deepgram ports Nova-3 speech-to-text to AWS Trainium2, gains 11–70% batch cost efficiency

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.

7.0