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AWS engineers published kernel optimizations for fused Mixture-of-Experts models on Amazon SageMaker AI, delivering up to 10% higher throughput and up to 9% faster Time to First Token on H200 GPUs.

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AWS Optimizes MoE Kernels on SageMaker: Up to 10% Higher Throughput on H200

What happened

FAQ

What is Mixture-of-Experts (MoE)?

An architecture where only a subset of experts is activated per input token, reducing effective compute compared to running all model parameters at every step.

What changed in AWS's new optimizations?

Three main changes: Swap-AB for better WGMMA utilization, Triton backend routing for FP8 quantization, and eliminating Triton JIT recompilation. Together they deliver up to 10% higher throughput and up to 9% faster Time to First Token on H200.

Do these optimizations help MENA AI teams?

Yes, if teams run fused MoE models on SageMaker AI, the throughput and latency gains translate directly into cost and user experience improvements, especially for chat and agent workloads.

Are the optimizations enabled automatically?

The post describes kernel-level engineering details; check the original source for how to apply or enable them in your SageMaker environment.

Source: AWS Machine Learning

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