HPE and NVIDIA Power “Mission” & “Vision”

/ AI, GPU Computing, Infrastructure, Networking

The News

HPE announced it will build two new supercomputers, Mission and Vision, for Los Alamos National Laboratory (LANL), in partnership with the U.S. Department of Energy, NNSA, and NVIDIA. The systems will be powered by the new HPE Cray Supercomputing GX5000, NVIDIA Vera Rubin GPUs, and Quantum-X800 InfiniBand, forming part of a $370 million DOE initiative to accelerate scientific research, AI development, and national security. To read more, visit the original press release here.

Analysis

The High-Performance Compute Wave Is Colliding With AI Demand

The HPE–NVIDIA partnership at LANL underscores a fundamental shift in the industry where supercomputing is no longer a domain reserved solely for physics and simulation. It is rapidly evolving into AI-native infrastructure, built to support generative modeling, agentic systems, multimodal workloads, and the fusion of simulation + AI in national security contexts.

As our research shows, AI workloads are pushing organizations toward more specialized and accelerated compute environments. The DOE’s investment signals that AI-driven research now requires infrastructure far beyond traditional GPU clusters. Developers in scientific computing increasingly expect architectures that can handle massive concurrency, exascale-level throughput, and hybrid workloads that combine numerical simulation with LLM-style reasoning.

Mission, which will deliver 4x the performance of the existing Crossroads system, reflects this trajectory. The Vision system, supporting unclassified AI research, highlights a second trend we’ve been documenting where we see expanded demand for shared, multi-tenant, AI-ready platforms capable of serving many research teams simultaneously without compromising performance or security.

This mirrors the broader market movement toward platforms that merge HPC, AI, and multidomain workflows under a common operational model.

The Application Development Landscape

HPE’s new Cray Supercomputing GX5000 architecture, debuting inside Mission and Vision, signifies a transition from traditional HPC nodes to AI-first system design. Direct liquid cooling, 25% higher density, and OCP-compliant server blades illustrate how the physical infrastructure is being shaped around large-scale AI workloads rather than retrofitted to them.

For application developers, particularly those working in simulation, scientific computing, and national security contexts, this new generation of systems may offer unprecedented access to high-performance AI pipelines. NVIDIA Vera Rubin GPUs are expected to enable deeper model parallelism and more efficient training, while Quantum-X800 networking should reduce cross-node contention, a major challenge for multimodal or agentic AI workloads.

The fact that Vision follows Venado (the HPE-built supercomputer initially used for unclassified research) also signals something important. AI workloads are diversifying beyond defense and classified missions, and researchers increasingly require environments that allow them to iterate quickly, share results, and collaborate across institutions. Developers may find these systems help accelerate prototyping, scaling, and validating AI models that are too large or too computationally intensive for cloud-based GPU clusters alone.

Why Advanced AI Infrastructure Is Becoming Necessary

The challenges that Mission and Vision aim to solve echo themes across our industry-wide research. Developers struggle to scale AI workloads due to GPU scarcity, networking limits, and fragmented compute environments. Even organizations with access to GPU clusters often lack the interconnected, high-bandwidth fabric needed for reliable multi-node training or inference at scale.

National labs and similar institutions face an additional constraint: data sovereignty and security. Unlike commercial enterprises, these organizations cannot freely transfer datasets across public clouds, forcing them to maintain on-prem, ultra-secure AI infrastructure. The DOE’s $370 million investment reflects a larger strategic imperative of ensuring that American AI research environments remain competitive with global capabilities while meeting the strictest data and security requirements.

These same pressures appear in enterprise contexts as well. theCUBE Research and ECI finds that 58.1% of organizations cite compliance and sovereignty as top considerations in their visibility and observability strategies, and over 70% plan to increase AI/ML spending in the next year. Mission and Vision represent the kind of integrated, high-density architecture many enterprises are trying to emulate at a smaller scale.

Developer Workflows Moving Forward

As supercomputing systems evolve into AI-native, multi-tenant architectures, developers may begin adopting practices that more closely resemble HPC workflows, such as batch scheduling, distributed parallelism, hybrid simulation + AI pipelines, regardless of whether they work in scientific computing or enterprise AI.

Mission’s performance leap could provide national security developers with the ability to run larger-scale predictive simulations enhanced by generative reasoning models, which is not something feasible on prior systems. Vision, meanwhile, may allow unclassified researchers to experiment with new training techniques, agentic models, and high-resolution simulations without resorting to less predictable cloud resources.

While outcomes will vary by team and workload, the combination of next-generation GPUs, high-density cooling, and networking optimized for multi-node acceleration could give developers more confidence that their large-scale AI workloads will train consistently, reproduce results, and scale under demanding conditions. It may also encourage more hybrid workflows where AI augments traditional physics-based simulation instead of replacing it.

Looking Ahead

The unveiling of Mission and Vision shows how national labs are preparing for a future where simulation, generative AI, and multimodal reasoning converge into shared research workflows. As developers lean into models that must interpret complex physics, manage massive datasets, and support agentic cycles, supercomputing and AI infrastructure will increasingly blend into a unified architectural category.

For HPE, the adoption of the Cray GX5000 architecture inside LANL’s next-generation systems could accelerate its role as a supplier of AI-native, exascale-class platforms. The collaboration with NVIDIA positions Mission and Vision as early examples of how future scientific research will operate: with AI woven directly into the computational fabric, supported by high-density GPUs, secure multi-tenant designs, and ultra-fast interconnects.

What comes next will depend on how effectively these architectures translate into developer productivity and if they can lead to faster experimentation, more consistent results, and the ability to tackle problems that today’s GPUs and networks simply can’t handle. With Mission and Vision, LANL is setting a template for what AI-accelerated national research may look like for the next decade.

Author

  • Paul Nashawaty

    Paul Nashawaty, Practice Leader and Lead Principal Analyst, specializes in application modernization across build, release and operations. With a wealth of expertise in digital transformation initiatives spanning front-end and back-end systems, he also possesses comprehensive knowledge of the underlying infrastructure ecosystem crucial for supporting modernization endeavors. With over 25 years of experience, Paul has a proven track record in implementing effective go-to-market strategies, including the identification of new market channels, the growth and cultivation of partner ecosystems, and the successful execution of strategic plans resulting in positive business outcomes for his clients.

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