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Project overview

Delfin-NG Solves the Resource GPU Crunch in Enterprise Environments

Delfin-NG is a distributed platform for the enterprise environment, which ties together scattered GPU resources from different Kubernetes clusters or standalone hosts and makes them available for GPU-based workloads.

 

Objectives

The key objectives of our Delfin-NG research is to:

  • Incentivizing GPU resource owners to temporarily make them available while still preserving control over them.
  • Leveraging under-utilized GPU resources in the enterprise across multiple geographical regions.
  • Enable resource utilization that is open, available, and auditable for transparency.
  • Develop a system, which is agnostic to GPU workload type and includes Machine Learning (ML), video processing, etc.
  • Adherence to regional data and locality restrictions as well as regulatory constraints.
  • Management of GPU resources with widely varying capabilities, such as architecture, number of cores, memory capacity, etc.
  • Consideration for the varying network latencies among GPU nodes.
  • The ability to trial various incentivization schemes.
     

Real world applications

Delfin-NG enables bundling islands of scattered GPU resources, making them available to the enterprise community. This provides a higher GPU utilization across the company, and thus, a better return on investment. This is especially useful for Large Language Models (LLMs) because it enables users to run large training jobs that could not otherwise be accommodated.

Project members