Overview

The Elastic GPU Service platform offers a system and workflows for efficient GPU resource management across one or more Kubernetes clusters.

The Elastic GPU Service (EGS) presents a valuable opportunity to address a critical gap in the current landscape of LLM-Ops tools and schedulers. Most tools in the LLM ecosystem focus on managing the lifecycle of large language models. But they often overlook the vital aspect of GPU scheduling and resource management across multiple users and clusters. Existing schedulers are similarly limited, primarily focusing on job scheduling within in-cluster GPU resources without addressing broader resource management across users and clusters. This has created a significant demand among cloud providers, particularly those serving large and medium-sized customers, for a robust provisioning and automation tool set. EGS meets this demand by providing pre-configured GPU nodes and pools, readily available for fine-tuning jobs, which improves GPU utilization and boosts monetization. Moreover, EGS enables cloud providers to deliver a premium, white-glove service to their larger customers. EGS also offers a self-service portal that simplifies and optimizes GPU resource management for a wider range of users.