Jan 15, 2026 (LinkedIn Post) – By Matt Swalley, Senior Director, VDURA

Neoclouds are purpose built to deliver GPU accelerated compute for AI training, fine tuning, inference, and serving. These environments are designed around high utilization, rapid provisioning, and concurrent workloads. As a result, the data infrastructure supporting Neoclouds must meet a different set of requirements than traditional enterprise or hyperscale storage architectures.

Learn more here: https://www.vdura.com/neocloud/

Neocloud Workloads and Data Characteristics

Neocloud environments typically operate with the following characteristics:

• High levels of concurrency across training, inference, and serving
• Continuous ingest of large datasets
• Frequent checkpointing and restart operations
• Multi-tenant usage with isolation requirements
• Constant infrastructure expansion as GPU fleets grow

In these environments, storage performance and consistency directly affect GPU utilization. Any bottleneck in the data path can reduce effective compute throughput and increase operational cost.

Data Platform Requirements for Neoclouds

Based on Neocloud usage patterns, a data platform must provide:

Consistent Performance at Scale

Performance must remain predictable as GPUs, users, and datasets increase. This includes both throughput and latency under concurrent access.

Independent Scaling of Performance and Capacity

Neocloud operators need to scale high performance flash independently from capacity oriented storage. This avoids over provisioning flash when retention requirements grow.

Always On Operations

Infrastructure expansion, maintenance, and upgrades must occur without downtime or data migration windows.

Multi-Tenant Support

Isolation between tenants is required to ensure predictable performance, security, and service level consistency.

Operational Simplicity

Neocloud teams typically operate lean. Automation and software driven management are required to reduce day two operational overhead.

How the VDURA Data Platform Aligns with Neocloud Needs

The VDURA Data Platform is designed to support these requirements through a distributed, scale out architecture.

Flash First Performance

NVMe flash tiers are used to deliver high throughput and low latency access for active AI workloads, including training data and checkpoints.

Hybrid Tiering for Retention

Capacity optimized tiers provide cost efficient storage for downstream workflows and long term retention without impacting active workload performance.

Independent Scale Out

Performance tiers, capacity tiers, and metadata services scale independently. New nodes can be added without disruption or rebalancing events.

Unified Namespace

Data remains accessible through a single global namespace across flash and capacity tiers, simplifying application workflows and data management.

Continuous Availability

The platform supports non disruptive expansion and maintenance, enabling NeoClouds to grow without service interruption.

Architectural Overview

The VDURA architecture combines distributed metadata services with flash and hybrid storage nodes connected over high speed network fabrics. This design allows the platform to sustain parallel access patterns common in AI pipelines while maintaining data consistency and resilience.

The result is a data layer that keeps pace with GPU driven workloads and supports the operational model of Neocloud providers.

Summary

Neocloud platforms are built around GPU performance and utilization. To operate efficiently at scale, they require a data platform that delivers consistent performance, independent scaling, multi-tenant isolation, and always on availability.

The VDURA Data Platform, as detailed on the NeoCloud solution page, is engineered to meet these requirements and support the growth of GPU focused cloud environments.

Learn more about VDURA for Neoclouds here: https://www.vdura.com/neocloud/