NVIDIA is strengthening its grip on the artificial intelligence software stack with the acquisition of SchedMD LLC, the company behind the widely used open source Slurm workload scheduler. The move highlights Nvidia’s growing emphasis on software and open source technologies as it seeks to reinforce its leadership in an increasingly competitive AI ecosystem dominated by large-scale training and inference workloads.
Best known for its high-performance GPUs, Nvidia has steadily expanded its focus beyond hardware, positioning software as a central pillar of its long-term strategy. Its proprietary CUDA platform continues to be a major draw for developers, while its expanding portfolio of open source AI models supports a wide range of applications, including physics simulations, robotics, and self-driving systems. Following the announcement, Nvidia shares rose 1.35%, supported by investor optimism and the company’s parallel release of new open source AI models.
In a blog post announcing the deal, the company underscored the strategic importance of Slurm within modern AI infrastructure. “Slurm, which is supported on the latest Nvidia hardware, is also part of the critical infrastructure needed for generative AI,” Nvidia said, highlighting the scheduler’s role in orchestrating large-scale AI training and inference workloads across complex compute environments.
Slurm is widely used across high-performance computing (HPC) clusters, research institutions, and enterprise data centers to efficiently manage compute resources. By bringing SchedMD into its fold, Nvidia gains deeper integration capabilities between its hardware, software frameworks, and the orchestration layer that governs how AI workloads are deployed and scaled.
Founded in 2010 by Morris “Moe” Jette and Danny Auble, SchedMD has built a strong reputation within the open source and HPC communities. Importantly, Nvidia confirmed that Slurm will continue to be distributed as open source software following the acquisition, with the company maintaining the same open access and community-driven development approach that has defined the platform to date.
The acquisition reflects Nvidia’s broader strategy of embedding itself across every layer of the AI stack, from silicon and systems to software platforms and developer tools. As competition intensifies among AI infrastructure providers, control over critical scheduling and workload management software could offer Nvidia an added advantage in optimizing performance, scalability, and efficiency for next-generation AI systems.
By doubling down on open source while deepening software integration, Nvidia is signaling that future AI leadership will be shaped as much by software ecosystems as by raw compute power.
