Conquer your storage challenges for generative AI success

HPE's Alletra Storage MP X10000 addresses the problems of AI data management

Partner Content The rapid growth of Generative AI (GenAI) is transforming industries, driving innovation, and creating unprecedented opportunities. But as organizations embrace this transformative technology, they're also grappling with the monumental challenges of managing the massive datasets GenAI relies on. From synthetic data generation to machine learning artifacts and retrieval augmented generation (RAG) workflows, the demand for modern solutions boasting intelligence services and performance at massive scale has never been greater.

According to Gartner1, two key predictions underscore the scale of change on the horizon:

• By 2029, key-value-based object storage will store 50 percent of on-premises unstructured data, up from less than 10 percent in 2025.

• By 2029, global demand for new storage capacity from generative AI will exceed 2 exabytes, up from less than 1 exabyte in 2024.

These projections align closely with what we're seeing at HPE. The adoption of GenAI applications has sparked an explosion in demand for high-performance, unstructured data storage capable of managing enormous volumes of AI datasets. Beyond storing data, organizations must contend with long-term retention requirements, complex regulatory compliance, and the need for instantaneous, high-quality responses from AI models—all of which demand storage solutions that go far beyond the capabilities of legacy systems.

Why legacy storage systems are holding you back

Older storage systems simply weren't built to handle the unique demands of AI. As organizations attempt to scale their AI initiatives, they're finding that legacy infrastructure introduces major roadblocks, slowing down RAG workflows while increasing operational costs, and hindering the agility needed for future success. Let's dig deeper into the limitations:

1. Performance bottlenecks: Traditional storage systems, often built on HDD-based architectures featuring outdated interfaces, struggle with the high throughput and low latency required for real-time AI workflows. These systems falter when handling the concurrent data requests generated by AI applications, leading to sluggish query processing, reduced responsiveness, and missed opportunities to deliver contextually relevant insights.
2. Scalability challenges: Legacy systems are inflexible, unable to scale seamlessly as data volumes grow. They often require disruptive or costly upgrades, making it difficult to efficiently manage AI pipelines and scaling demands.
3. Inadequate indexing and metadata management: AI models like large language models (LLMs) rely on curated, indexed datasets for instantaneous data retrieval. Older storage systems typically store data in static formats, requiring extensive pre-processing on separate infrastructure. This process is time-consuming, resource-intensive, and prone to errors.

The solution: modern, AI-ready object storage

To maximize efficiency and performance, Gartner recommends the following best practices for building GenAI data stores2:

• Deploy key-value-based object storage with integrated intelligence and multi-protocol access when building new GenAI application data stores to improve on cost and performance.

• Move GenAI data preparation tasks from data analytics applications directly into the storage platform to improve data pipeline efficiency and cost.

Unlocking the full potential of GenAI and RAG workflows requires more than just storage. It demands a purpose-built solution tailored to the complexities of AI. Enter HPE Alletra Storage MP X10000, a groundbreaking platform designed to address the unique challenges of AI data management.

What makes the X10000 stand out?

The X10000 is engineered with a state-of-the-art architecture that merges high-performance object storage with intelligent data services. This innovative design ensures ultra-fast data ingestion, seamless scalability, and real-time insights. Here's how it solves the most pressing AI data challenges:

Intelligent data services:

• Inline metadata enrichment: As data is ingested, intelligent automated scanning processes create enriched metadata such as vector embeddings in near-real time for use in GenAI, RAG, and analytics applications. This accelerates AI workflows by ensuring data is ready in place for inference without extensive pre-processing.

• Model Context Protocol for agentic AI: A built-in MCP server streamlines integration between LLMs and external data sources, reducing complexity and enabling faster, more reliable training cycles and AI insights.

• SDK for NVIDIA AI Data Platform: Seamlessly integrating with NVIDIA's AI Data Platform reference design, the X10000 accelerates intelligent pipeline orchestration for agentic AI, simplifying unstructured data pipelines for ingestion, training, and inference.

Unparalleled performance:

• Log-structured key value store: This foundational data layer is optimized for flash access, enabling the X10000 to reduce write amplification, deliver predictable performance, and achieve ultra-fast data ingestion and retrieval critical for RAG workflows. Every node adds a proportionately linear amount of performance to the system.

• First-class citizen protocols: Built on the key-value store, the X10000 features native protocol-specific namespace layers, such as object and file, designed to operate independently and at peak performance. Each protocol is treated as a "first-class citizen," meaning it's fully optimized for its unique requirements without being hindered by the inefficiencies of layered architectures.

• All-NVMe: The X10000's all-flash design delivers up to 6x faster performance than competitors without relying on front-end caching or data movement between media.

• RDMA for Object integration: Collaborating with NVIDIA, HPE enables low-latency remote direct memory access (RDMA) between GPUs, system memory, and the X10000. This eliminates CPU and TCP/IP bottlenecks, allowing AI applications to access massive datasets almost instantaneously.

Seamless scalability:

• Disaggregated storage design: A modular architecture allows you to scale compute and capacity independently, ensuring flexibility and cost efficiency as AI workloads evolve.

• Linear performance scaling: Every node added to the cluster proportionally boosts performance, enabling organizations to handle millions or even billions of data points without disruption.

Streamlined Management and enterprise-grade resilience

• Managing cutting-edge infrastructure doesn't have to be complex. With HPE GreenLake cloud, the X10000 delivers a simplified management experience across the entire lifecycle, from installation to provisioning to upgrades. Non-disruptive, in-place upgrades and proactive support ensure seamless operations and minimal downtime, while enterprise-grade resiliency safeguards your data integrity.

Why the X10000 is the future of storage for AI

As AI continues to reshape the data landscape, you need storage solutions that don't just keep pace, but solutions that can drive innovation and unlock new possibilities. HPE Alletra Storage MP X10000 combines advanced architecture, intelligent data services, and a modular, scalable design to empower your businesses to:

• Accelerate RAG pipelines and inference cycles

• Seamlessly handle exabyte-scale datasets

• Optimize AI-readiness and performance

• Reduce operational complexity and costs

• Future-proof your infrastructure for the next wave of AI-driven innovation

Unlock the power of your data with HPE

The AI era is now. The ability to manage and leverage data efficiently will define the leaders of tomorrow. Don't let legacy systems hold you back. With the X10000, HPE is setting a new standard for AI-ready storage to help you transform your data into actionable insights, faster and more effectively than ever before.

Learn more

Read this report to find out why Gartner believes "Key-value-based object storage combined with integrated data intelligence, which adds context and meaning to the underlying data, best supports GenAI applications."3 

1 Gartner, Enhance Generative AI Data Management With Intelligent Storage, Chandra Mukhyala, 24 June 2025

2 Gartner, Enhance Generative AI Data Management With Intelligent Storage, Chandra Mukhyala, 24 June 2025

3 Gartner, Enhance Generative AI Data Management With Intelligent Storage, Chandra Mukhyala, 24 June 2025

Contributed by HPE.