Stop hand-tuning kernels: How Neuron Agentic Development accelerates AWS Trainium optimizations | Amazon Web Services
As frontier AI models grow in scale and complexity, developers face a common challenge across every hardware platform: how do you extract the maximum performance and efficiency from the silicon their models run on. Whether delivering real-time experiences for world models, supporting deeper reasoning in agentic workflows, or reducing inference costs at scale, the gap between what hardware can theoretically deliver and what most teams achieve remains significant. Custom kernel development has historically been the path to closing that gap, but it demands deep architectural expertise, manual profiling workflows, and iterative optimization cycles that few teams can afford.
This doesn’t need to be the case. What if every machine learning (ML) engineer could operate as a performance engineer, writing hardware-aware kernels, diagnosing bottlenecks, and shipping optimized models, without years of chip-level experience? What if developers already proficient on one architecture could ramp up on another in days instead of months?
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