Advances in Inference Disaggregation and Load Balancing for AI Workloads

The field of AI is witnessing significant advancements in inference disaggregation and load balancing, driven by the need for improved throughput and interactivity in large-scale deployments. Researchers are exploring novel architectures and techniques to optimize the performance of large language models and other AI workloads. A key direction is the development of temporally-disaggregated pipeline parallelism, which aims to eliminate pipeline bubbles and improve throughput. Another area of focus is congestion-aware path selection for load balancing in AI clusters, which can significantly reduce flow completion times. Noteworthy papers include:

  • BestServe, which presents a novel framework for ranking serving strategies by estimating goodput under various operating scenarios, and
  • TD-Pipe, which proposes a temporally-disaggregated pipeline parallelism architecture for high-throughput LLM inference, achieving significant throughput improvements over existing approaches.

Sources

Beyond the Buzz: A Pragmatic Take on Inference Disaggregation

BestServe: Serving Strategies with Optimal Goodput in Collocation and Disaggregation Architectures

Congestion-Aware Path Selection for Load Balancing in AI Clusters

TD-Pipe: Temporally-Disaggregated Pipeline Parallelism Architecture for High-Throughput LLM Inference

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