i14 and AWS describe changes to Laplace 3B for AWS Trainium2
The companies i14 and AWS described porting Laplace 3B to AWS Trainium2 using the beta version of TorchNeuron Native. According to the authors, training reached approximately 120 000 tokens per second; the recurrent state occupies 29.5 MB per sequence regardless of context length.
The companies i14 and AWS published a case study on adapting Laplace 3B for training and running on AWS Trainium2 accelerators. The team rewrote the original Gated Linear Attention (GLA) kernel from a specialized GPU implementation using standard PyTorch operations. It then chose the beta version of TorchNeuron Native, which, according to the authors, delivered better performance in a comparison of approaches. The team retained both the GLA architecture and the Muon optimizer.
Laplace 3B uses a linear attention architecture that stores previous context in a continuously updated state. According to the authors, this state occupies 29.5 MB per sequence regardless of context length. However, the history is compressed, so the authors recommend verifying information retrieval quality for specific tasks.
According to the authors, training on a single trn2.48xlarge instance reached approximately 120 000 tokens per second. During generation, they measured 11 544 output tokens per second with 1 280 concurrent sequences. The instance contains 16 Trainium2 chips and 64 logical NeuronCores. Details are available in the source article.
Why it matters
The case study offers a concrete process for teams looking to adapt their own model architecture for AWS Trainium2. The fixed recurrent state size may make it easier to plan memory capacity for long-running agents and large-document analysis; suitability also depends on how well the model handles the compressed history.
Two audiences, two different impacts
What this means
For individuals
A developer adapting a custom architecture gets an example of how to rewrite a specialized kernel using standard PyTorch operations and use the beta version of TorchNeuron Native.
For a business
For a company running applications with long contexts, a predictable recurrent state size is important. When assessing capacity, however, it must also consider the quality of information retrieval from the compressed history.
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