Taming GPU Underutilization via Static Partitioning and Fine-grained CPU Offloading

The project addresses a significant bottleneck in high-performance computing (HPC): the rigidity of NVIDIA's Multi-Instance GPU (MIG) partitioning. While MIG provides hardware-level isolation, its static nature often leads to waste when workloads don't perfectly fit the slice. This project introduces a hybrid approach of partitioning and CPU offloading. However, the defensibility is currently very low (score 2) as it is a brand-new research artifact (8 days old, 0 stars) with no community traction beyond 4 forks, likely from collaborators. The primary threat comes from NVIDIA itself; as the hardware vendor and creator of MIG, they are best positioned to implement dynamic partitioning or more efficient unified memory management (e.g., through Grace-Hopper architectures) that would render this specific offloading technique obsolete. Furthermore, GPU orchestration platforms like Run:ai or Kubernetes-based slicers (Volcano, KubeShare) are the natural homes for this logic, making it difficult for a standalone research project to maintain a moat. The displacement horizon is set at 1-2 years, coinciding with the broader rollout of unified memory systems and next-gen GPU scheduling in production clusters.