NexusGPU/tensor-fusion

Tensor-fusion targets the high-value problem of GPU underutilization by creating an abstraction layer between the application and the physical GPU. While the technical complexity of intercepting CUDA calls and managing remote memory is high (usually requiring deep kernel/driver knowledge), the project's defensibility is hampered by its current lack of momentum (0.0 velocity) and moderate star count (138) over 500+ days. In the GPU infrastructure space, 'software rot' occurs quickly as NVIDIA releases new driver versions and hardware architectures (H100/B100). The project faces massive platform risk from NVIDIA itself (MIG, vGPU, and the acquisition of Run:ai) and cloud providers who are building proprietary pooling layers. Competing open-source or commercial-grade projects like Juice Labs, SkyPilot (for orchestration), or even Ray provide similar or superior pooling capabilities with significantly more active maintenance. Without a surge in developer activity to keep up with the CUDA lifecycle, this project risks becoming a reference implementation rather than a production-grade tool.