Amazon SageMaker AI
A managed machine learning platform under Amazon AWS that provides capabilities for model training, deployment, iteration, and online learning. It supports enterprise-scale implementation of reinforcement learning, contextual bandit algorithms, and related approaches for production use cases.
Core Facts
Timeline (last 90 days)
The AWS engineering team carried out kernel-level optimizations on Amazon SageMaker AI for a fused MoE model, including Swap-AB to improve WGMMA utilization, Triton backend routing to implement FP8 quantization, and eliminating repeated Triton JIT compilation.
The three kernel optimizations can be orthogonally stacked with upper-layer optimizations, and for teams deploying MoE models on SageMaker AI, these optimizations will likely be reflected in service performance in a managed form.
These optimizations brought up to a 10% throughput improvement and up to a 9% improvement in time to first token (TTFT) on H200 GPUs.
The Amazon Payments team used multi-objective contextual bandits on Amazon SageMaker AI to personalize the customer acquisition funnel, achieving close to a double-digit conversion rate improvement on a specific audience segment.
The managed training environment provided by SageMaker AI can reduce the engineering complexity of building an RL pipeline.
The core of this solution is multi-turn reinforcement learning (MTRL), leveraging Amazon SageMaker AI to complete the end-to-end training pipeline.
CPU-GPU hybrid inference (offloading) introduces a significant drop in inference speed.
All Facts (7)
CPU-GPU hybrid inference (offloading) introduces a significant drop in inference speed.
75%UnverifiedThese optimizations brought up to a 10% throughput improvement and up to a 9% improvement in time to first token (TTFT) on H200 GPUs.
50%UnverifiedThe AWS engineering team carried out kernel-level optimizations on Amazon SageMaker AI for a fused MoE model, including Swap-AB to improve WGMMA utilization, Triton backend routing to implement FP8 quantization, and eliminating repeated Triton JIT compilation.
50%UnverifiedThe Amazon Payments team used multi-objective contextual bandits on Amazon SageMaker AI to personalize the customer acquisition funnel, achieving close to a double-digit conversion rate improvement on a specific audience segment.
50%UnverifiedThe managed training environment provided by SageMaker AI can reduce the engineering complexity of building an RL pipeline.
50%UnverifiedThe core of this solution is multi-turn reinforcement learning (MTRL), leveraging Amazon SageMaker AI to complete the end-to-end training pipeline.
50%UnverifiedThe three kernel optimizations can be orthogonally stacked with upper-layer optimizations, and for teams deploying MoE models on SageMaker AI, these optimizations will likely be reflected in service performance in a managed form.
50%