FOURTH PARADIGM has unveiled a breakthrough in GPU dynamic scheduling—integrating compute and memory allocation through Kubernetes Dynamic Resource Allocation (DRA). This innovation addresses the industry's demand for flexible GPU resource partitioning, marking a shift from capacity expansion to multi-dimensional resource coordination.

With the stable release of Kubernetes v1.34, DRA enables smarter cloud-native resource management. Leveraging its expertise in AI infrastructure, FOURTH PARADIGM has optimized its HAMi-Core framework with DRA compatibility and introduced a GPU Dynamic Resource Driver. This allows GPUs to be shared and scheduled as flexibly as CPUs, advancing cloud-native compute management for AI workloads.

**Smarter GPU Utilization, Higher Efficiency** GPUs serve as the "engine" for AI training and inference, yet traditional usage often leads to underutilization or wasteful exclusivity. By combining HAMi-Core with DRA, FOURTH PARADIGM enables dynamic GPU partitioning and multi-task parallelism. Multiple jobs can now share a single GPU with tailored compute and memory allocations—mirroring CPU efficiency.

Key improvements include: 1. **Task Scheduling**: - *Past*: Sequential execution on GPU nodes, forcing queued tasks to wait. - *Now*: Parallel scheduling slashes wait times and boosts cluster throughput.

2. **Resource Allocation**: - *Past*: Fixed GPU configurations, akin to rigid "set menus." - *Now*: Customizable GPU performance tiers ("mixed hands") match precise compute needs, minimizing waste.

These enhancements streamline large-scale deployments, improving operational efficiency and user experience.

**Evolving Cloud-Native AI Infrastructure** As an open-source project led by FOURTH PARADIGM, HAMi-Core elevates GPU utilization in containerized environments. Its DRA integration automates resource allocation via standardized workflows, lowering adoption barriers. This reflects FOURTH PARADIGM’s dual commitment to AI infrastructure innovation and open-source collaboration.

**Open-Source Collaboration for Future Innovation** The demo project is now available on GitHub (Project-HAMi/k8s-dra-driver), with ongoing development in the HAMi community. Developers and enterprises are invited to contribute, driving GPU scheduling toward a more intelligent and open future.