AWS Neuron 2.32 adds sparse-model kernels and variable-size collectives
AWS Neuron 2.32 added on-device top-K, variable-length all-gather, data-dependent loops, thirteen MoE and sparse-attention kernels including DeepSeek V3.2 and MXFP8 paths, variable-size Trainium collectives, and an updated vLLM Neuron plugin.
Why it matters: These are concrete compiler and runtime building blocks for training and serving sparse or mixture-of-experts models on AWS Trainium hardware.