Nvidia, the leading force in AI chips, announces its Nvidia SchedMD acquisition on December 15, 2025. The company acquires SchedMD, the developer behind the popular open-source workload manager Slurm. This move bolsters Nvidia’s open-source AI ecosystem and optimizes large-scale computing for generative AI training and inference.
Nvidia doubles down on software investments amid growing competition. The acquisition helps the company integrate critical tools that manage massive AI workloads across data centers and supercomputers.
Nvidia SchedMD Acquisition: Why Slurm Matters in AI and HPC
Slurm powers resource scheduling in high-performance computing (HPC) and AI environments. Developers and enterprises use it to allocate jobs efficiently on clusters with thousands of GPUs.
Key highlights of the Nvidia SchedMD acquisition include:
- Nvidia keeps Slurm fully open-source and vendor-neutral for broad accessibility.
- The company invests further in Slurm development to support the latest Nvidia hardware.
- Nvidia accelerates optimizations for heterogeneous clusters, mixing various hardware types.
- Existing customers, including cloud providers like CoreWeave and research centers, receive continued support, training, and updates.
- Slurm runs on over half of the top 10 and top 100 supercomputers in the TOP500 list.
Nvidia and SchedMD collaborate for over a decade. SchedMD CEO Danny Auble calls the deal “validation” of Slurm’s role in demanding AI setups. Nvidia states Slurm forms essential infrastructure for foundation model builders.
The acquisition follows Nvidia’s release of new open-source AI models. It counters rivals, including Chinese labs, by enhancing efficiency in AI infrastructure. Financial terms stay undisclosed. Nvidia shares rise 1.35% after the news.
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Founded in 2010 by Slurm creators Morris Jette and Danny Auble, SchedMD employs about 40 people. The deal positions Nvidia as a full-stack AI leader, from chips to orchestration software.
This Nvidia SchedMD acquisition signals deeper control over AI’s foundational layers. Enterprises gain faster innovations in workload management, driving productivity in supercomputing and generative AI.
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