How Volcano Addresses LLM Training and Inference Challenges

The increasing adoption of large language models (LLMs) has heightened the demand for efficient AI training and inference workloads. As model size and complexity grow, distributed training and inference have become essential. However, this expansion introduces challenges in network communication, resource allocation and fault recovery within large-scale distributed environments. These issues often create performance bottlenecks that hinder scalability.

Addressing Bottlenecks Through Topology-Aware Scheduling

In LLM training, model parallelism…