Nvidia is expanding its presence across 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 focus on software and open source technologies as competition intensifies in large scale AI training and inference.
Known primarily for its high performance GPUs, Nvidia has steadily shifted beyond hardware, placing software at the core of its long term strategy. Its proprietary CUDA platform remains a key attraction for developers, while its growing portfolio of open source AI models supports applications such as physics simulations, robotics, and self driving systems. Following the announcement, Nvidia shares rose 1.35 percent, supported by investor optimism and the company’s parallel release of new open source AI models.
In a blog post announcing the acquisition, Nvidia highlighted the importance of Slurm in modern AI infrastructure. “Slurm, which is supported on the latest Nvidia hardware, is also part of the critical infrastructure needed for generative AI,” the company said, pointing to the scheduler’s role in managing large scale AI training and inference workloads across complex computing environments.
Slurm is widely used across high performance computing clusters, research institutions, and enterprise data centres to efficiently allocate and manage computing resources. With SchedMD now part of Nvidia, the company gains tighter integration between its hardware, software frameworks, and the orchestration layer that controls how AI workloads are deployed and scaled.
SchedMD was founded in 2010 by Morris “Moe” Jette and Danny Auble and has built a strong reputation within the open source and HPC communities. Nvidia confirmed that Slurm will continue to be distributed as open source software, maintaining the same open access and community driven development model that has defined the platform so far.
The acquisition reflects Nvidia’s broader strategy to establish itself across every layer of the AI stack, from silicon and systems to software platforms and developer tools. As competition among AI infrastructure providers grows, control over critical scheduling and workload management software could give Nvidia an added edge in improving performance, scalability, and efficiency.
By strengthening its software integration while continuing to support open source, Nvidia is signalling that future AI leadership will depend as much on software ecosystems as on computing power.
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