AWS boosts MoE throughput 10% with SageMaker kernel optimizations
Mixture-of-Experts kernel engineering on Amazon SageMaker AI
The techniques target the exact bottlenecks that slow self-hosted MoE inference, and they are documented well enough to reproduce in your own SageMaker deployments. Teams serving large sparse models on NVIDIA H200 instances can apply them directly to cut latency and cost per token. Compared with relying on a hosted model API, this matters most for builders who need control over quantization and serving internals.
AWS engineers published kernel engineering techniques for running fused Mixture-of-Experts (MoE) models on Amazon SageMaker AI. The optimizations include Swap-AB to improve WGMMA utilization, a Triton backend routing approach for FP8 quantization, and a method to eliminate Triton JIT recompilation. Together, these changes deliver up to 10% higher throughput and up to 9% faster Time to First Token on H200 GPUs.
Mixture-of-Experts kernel engineering on Amazon SageMaker AI
AWS engineers share kernel optimizations for fused Mixture-of-Experts (MoE) models on Amazon SageMaker AI: Swap-AB for 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 GPUs.
- vLLM10-15 08:39原文