Developer ports Gemma-4 31B to AWS Inferentia2, achieving token-perfect output after key fix

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A developer successfully ported Google's Gemma-4 31B dense language model to AWS Inferentia2 hardware, achieving greedy-decode output identical token-for-token to a CPU fp32 reference. The 30-billion-parameter model presented scale challenges, as its 60 GB bf16 size cannot fit on a single 16 GB NeuronCore, requiring tensor parallelism across 8 ranks. Initial attempts using manual parallel tracing failed due to out-of-memory errors and deadlocks during multi-rank compilation. The breakthrough came by switching to NxD's ModelBuilder, which compiles a single rank and shards weights per rank, eliminating the 8x simultaneous compile overhead. The final compiled model took roughly 39 minutes to build, peaked at 182 GB host memory, and produced a 108 GB output file containing the full graph and all 8 ranks' sharded weights.

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