Amazon SageMaker HyperPod now supports disaggregated prefill and decode
Amazon SageMaker HyperPod now supports Disaggregated Prefill and Decode (DPD), an inference optimization that separates the two phases of large language model (LLM) inference — prefill and decode — onto dedicated GPU pools and transfers the key-value (KV) cache between them over Elastic Fabric Adapter (EFA) using GPU-Direct RDMA. Customers running LLMs in production for chat assistants, agentic pipelines, retrieval-augmented generation, and long-document analysis need consistent per-token latency and predictable throughput under mixed traffic, but when prefill and decode share the same GPU, a
The increasing complexity and scale of LLM deployments in production environments necessitate continuous optimization for performance and cost-efficiency.
This optimization directly addresses critical performance bottlenecks for large language models, improving capabilities for real-time generative AI applications and reducing operational costs.
LLM inference can now be more efficiently managed and scaled by separating prefill and decode stages, leading to more consistent latency and predictable throughput, especially under mixed traffic conditions.
- · AWS
- · Companies deploying large LLMs in production
- · Generative AI application developers
- · GPU manufacturers
- · LLM inference platforms without similar optimizations
- · Companies with less efficient LLM infrastructure
Improved performance and cost-effectiveness of LLM inference on AWS.
Acceleration of new real-time AI applications that require highly consistent LLM responses.
Increased adoption of agentic AI systems and complex RAG pipelines due to enhanced underlying infrastructure efficiency.
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