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Disaggregated Prefill and Decode for LLM Inference on SageMaker HyperPod

AWS has introduced a method to separate the prefill and decode phases of large language model inference on SageMaker HyperPod. By running each phase on dedicated GPU pools connected via Elastic Fabric Adapter with RDMA, the approach eliminates interference that occurs when the phases share a single GPU. This results in more consistent per-token latency and independent scaling for long-context, high-concurrency workloads.

Jul 106 minNeura News

AI Models

Tiny-vLLM Inference Engine and Course Released on GitHub

A developer has released tiny-vllm, a high-performance LLM inference engine written in C++ and CUDA, alongside a comprehensive course that walks through implementing the engine from scratch. The project covers loading models from Safetensors, running prefill and decode, building CUDA kernels, KV cache, and more. It is intended as a learning resource for engineers and educators.

May 304 minNeura News

AI Tools

AWS details observability strategy for SageMaker AI LLM inference

AWS published a technical guide on comprehensive observability for large language models deployed on Amazon SageMaker AI. The approach separates monitoring into infrastructure quantity and LLM quality dimensions, using CloudWatch and Amazon Managed Grafana to visualize GPU utilization, cost, and response quality signals. The solution includes alert thresholds for metrics such as safety scores and composite quality scores, evaluated by an LLM-as-judge setup.

May 304 minNeura News

AI News

Latest News

Disaggregated Prefill and Decode for LLM Inference on SageMaker HyperPod

Tiny-vLLM Inference Engine and Course Released on GitHub

AWS details observability strategy for SageMaker AI LLM inference