HPE Alletra Storage MP X10000 is designed to bring data intelligence directly into the storage layer so you don’t have to bolt on separate data enrichment stacks.

Key capabilities that help make data AI-ready:

• Inline metadata enrichment: A built-in data intelligence node enriches metadata as data lands, so you can search, filter, and organize content more efficiently for AI and analytics.
• Native vectorization and indexing: Data is automatically prepared for use with Retrieval Augmented Generation (RAG), large language models (LLMs), and advanced analytics, reducing the need for external preprocessing pipelines.
• Model Context Protocol (MCP) support: With MCP server support, the data intelligence node can expose precise contextual information as a resource to other applications or agents, effectively turning your storage into a source of high-quality context for AI insights and predictions.
• Closer to compute: By keeping enrichment and vectorization close to where data is stored, you avoid moving large volumes of data across multiple processing stacks, which can be slow, costly, and risky.

The result is a storage platform that helps you reimagine how data is prepared and delivered for AI, cutting down on infrastructure sprawl while improving the speed, quality, and relevance of the data your models consume.

HPE Alletra Storage MP X10000 is built to handle both modern data protection and AI workloads on the same platform, with a focus on performance and resilience.

For backup and recovery:

• High backup throughput: Delivers up to 2.5 PB/hour of backup storage throughput, helping you shorten backup windows.
• Strong data reduction: Achieves up to 60:1 data reduction, which can significantly lower storage consumption and cost.
• Fast restores: Flash-based architecture supports rapid data restores to help you recover quickly from outages and disasters, including ransomware events.

For AI and analytics performance:

• Up to 6x performance advantage: Uses a log-structured key-value store to fully exploit flash media, improving throughput for data-intensive applications.
• RDMA for S3 and file I/O: Enables a direct path between GPU memory, system memory, and the X10000, which can deliver up to 20x faster time to first token for AI inference.
• NVIDIA-Certified Storage: Validated at the Foundation level for object-based systems, confirming that the storage layer can efficiently feed accelerated compute for faster training, lower-latency inference, and higher GPU utilization.

Data protection and security:

• Enterprise-class security features including FIPS certification, detailed monitoring, versioning, object lock-based immutability, object replication, and auditing.
• Data-in-place and TLS encryption, plus external key manager support for self-encrypting drives.
• Erasure coding for data protection and always-on processes to help ensure data integrity.

Together, these capabilities allow you to run backup, recovery, and AI workloads on a single platform while maintaining strong performance, protection, and operational efficiency.

HPE Alletra Storage MP X10000 is designed to help you rethink how you scale and operate storage across hybrid environments, especially for AI and analytics.

Scalability and flexibility:

• Disaggregated architecture: Independently scale performance and capacity using separate compute (nodes) and capacity (all-NVMe JBOF) building blocks.
• Drive options: Choose TLC or QLC drives to align performance and cost with workload needs.
• Start small, grow over time: Begin with as few as three nodes and expand without disruption, maintaining storage efficiency and high availability.
• Unified file and object foundation: Integrates with HPE Data Fabric Software so users and AI models can consistently access data across your hybrid estate while simplifying governance and security.
• No tiering complexity: Keep unstructured file and object data on a single tier of fast, scalable, all-NVMe flash to avoid silos and complex data movement workflows.

Cost efficiency and sustainability:

• Efficient inline data reduction and erasure coding help cut storage costs, reduce energy use, and shrink your data footprint.
• Disaggregated scaling helps avoid overprovisioning by letting you add only what you need.

Cloud-like operations with HPE GreenLake:

• Unified cloud experience: Manage block, file, and object workloads on common hardware through a single, intuitive cloud interface—from installation to provisioning to upgrades.
• Consumption-based model: GreenLake Flex supports pay-as-you-go procurement so you can scale storage and compute up or down and pay only for the capacity consumed.
• On-premises cloud experience: GreenLake Dedicated Platform brings a modern cloud experience to on-premises environments, including regulated or disconnected sites.

Operational assurance:

• 100% Data Availability Guarantee for CapEx- and Flex-based purchases, providing added assurance for production file and object workloads.
• 90-day satisfaction guarantee to evaluate the platform in your own environment with reduced deployment risk (subject to minimums or reserve capacity).

In combination, these features give you a hybrid-ready, cloud-operated storage platform that can scale with your AI and analytics initiatives while keeping costs and complexity in check.