By Molly Presley, SVP of Global Marketing, Hammerspace

GPU-Centric Data Orchestration Becomes Top Priority

As we head into 2025, one of the challenges in AI and machine learning (ML) architectures continues to be the efficient movement of data to and between GPUs, particularly remote GPUs. GPU access is becoming a critical architectural concern as companies scale their AI/ML workloads across distributed systems. Traditional data orchestration solutions, while valuable, are increasingly inadequate for the demands of GPU-accelerated computing.

The bottleneck isn't just about managing data flow-it's specifically about optimizing the transport of data to GPUs, often to remote locations, to support high-performance computing (HPC) and advanced AI models. As a result, the industry will see a surge in innovation around GPU-centric data orchestration solutions. These new systems will focus on minimizing latency, maximizing bandwidth, and ensuring that data can seamlessly move across local and remote GPUs.

Companies already recognize this as a key issue and are pushing for a rethinking of how they handle data pipelines in GPU-heavy architectures. Expect to see increasing investment in technologies that streamline data movement, prioritize hardware efficiency, and enable scalable AI models that can thrive in distributed and GPU-driven environments.

Breaking Down Data Silos Will Become a Central Focus for AI and Data Architects

In 2025, breaking down data silos will emerge as a critical architectural concern for data engineers and AI architects. The ability to aggregate and unify disparate data sets across organizations will be essential for driving advanced analytics, AI, and machine learning initiatives. As the volume and diversity of data sources continue to grow, overcoming these silos will be crucial for enabling the holistic insights and decision-making that modern AI systems demand.

The focus will shift from the infrastructure toward the seamless integration of data across various platforms, teams, and geographies. The goal will be to create an ecosystem where data is easily accessible, shareable, and actionable across all domains. Expect to see new tools and frameworks aimed at simplifying data integration and fostering greater collaboration across traditionally siloed environments.

Enterprise HPC Must Align with Standardized Technologies for Unstructured Data Processing

By 2025, medium to large enterprises will face a pivotal challenge: integrating high-performance computing (HPC) for unstructured data processing while adhering to enterprise standards. As organizations increasingly rely on AI and data analytics to gain a competitive edge, the need to process vast amounts of unstructured data-like text, images, and video-will be unavoidable. However, enterprises have long struggled to adopt HPC at scale due to the complexities of reconciling specialized HPC technologies with enterprise requirements for security, compliance, and operational standards.

The solution lies in the development of HPC technologies designed to work within enterprise-standard environments. In 2025, we expect to see the rise of enterprise-ready HPC solutions that seamlessly integrate with standard clients, operating systems, networks, and security frameworks. This convergence will enable organizations to finally leverage HPC for large-scale unstructured data processing without compromising on enterprise security, compliance, or performance standards.

Power Efficiency in 2025: Fueling Growth, Innovation, and Green Initiatives

In 2025, power efficiency will be driven by financial and environmental imperatives as well as the desire to have the power to have more compute available for data analytics and AI. Companies will increasingly focus on optimizing energy use, not just to meet green initiatives but to unlock the computational power needed for intensive GPU workloads. This shift goes beyond the surface-level appeal of sustainability. Power efficiency will become a strategic advantage, enabling businesses to save on operational costs while expanding their computational capabilities. As an added benefit, organizations will be able to meet their green goals, creating a win-win situation: enhanced profitability alongside a reduced environmental footprint.

In this landscape, energy-efficient data storage and management infrastructure won't just make companies look good-it will become a critical enabler for growth and innovation, positioning them at the forefront of both technological and environmental progress.

2025: The Rise of Collaborative Global Namespaces

In 2025, the importance of how companies manage global namespaces will reshape data-handling strategies across the industry. Not all global namespaces will be created equal: some will offer only read-only capabilities, while others will enable active read-write functionality. While having a single view of your data sounds efficient, its true value lies in the ability to act on that data seamlessly. If teams can't collaborate in real time on a single dataset without creating multiple copies that require complex merging, it defeats the purpose of streamlined data management.

The challenge of copy proliferation - where multiple users create their own versions of the same dataset for individual read-write tasks - can introduce inefficiencies, silos, and data inconsistencies. As companies look to build more collaborative, efficient data environments, they will need to prioritize implementing global namespaces that not only allow for unified data views but also support active read-write capabilities. In this way, businesses can avoid data fragmentation while enabling seamless collaboration, making their data infrastructure both elegant and functional for modern workloads.

GPU Demand Soars, but AI Adoption has Companies Rethink Resource Allocation

As we enter 2025, the AI industry faces an unexpected situation: there's a huge demand for GPUs worldwide, yet many of these powerful chips aren't being fully used. While companies invested heavily in GPU-based infrastructure, many continue to struggle to apply these chips to AI workloads, instead redirecting them toward non-AI applications. The expected AI-driven boom remains slower than anticipated.

We will continue to see companies be more selective with GPU allocations, as companies focus on areas where the impact of AI in areas like data analytics and cloud computing enhancements - rather than emerging AI initiatives. Additionally, as developers become more resource-conscious, the focus on optimizing algorithms for available hardware, leveraging CPU-bound AI, and adopting hybrid approaches could become central trends. Ultimately, 2025 may be a year that companies adapt to both the technical and logistical challenges of realizing AI's potential.

##

ABOUT THE AUTHOR

Molly is the SVP of Global Marketing for Hammerspace and host of the very popular Data Unchained podcast. She brings a wealth of experience from leading product and marketing organizations, user communities, and customer advisory board for global technology innovators. Molly is the founder of the Active Archive Alliance, co-author of three books focused on putting data to use in research, analytics and AI environment, and was previously a board member of the Storage Networking Industry Association (SNIA).