Qumulo’s new Helios AI Agent, Cloud AI Accelerator and AI Networking give users oversight, scalability and speed needed to fully control innovation on their data, at any location.
A key component of Cloud Data Fabric, Qumulo NeuralCache gives low-latency access even to remote data, which is critical for AI models.
Unstructured data management specialist Qumulo has developed three major systems that support enterprises as they integrate their data into AI factories. An AI Factory is a scalable, automated system used to continuously train, deploy and improves artificial intelligence models using data pipelines, feedback loops and infrastructure to optimize performance and relevance across applications. It can also simplify day-to-day operations by downgrading smaller‑scale decisions to machine learning algorithms.
Qumulo’s new developments – Helios Agent, Qumulo Cloud AI Accelerator and Qumulo AI Networking – are intended to give users the oversight, scalability and speed needed to fully control innovation on their data, at any location.
Qumulo Helios, Cloud AI Accelerator and AI Networking serve as intelligent, distributed, autonomous data infrastructure. They extend Qumulo's platform, which unifies exabyte-scale data across cloud, edge and data centre environments, while continuously learning, optimizing and accelerating the workflows of data-intensive organizations.
"Enterprises in today's competitive environments need more than just storage – they need systems that think, adapt and accelerate," said Qumulo CEO Douglas Gourlay. "Helios gives users predictive awareness of their entire data ecosystem, Cloud AI Accelerator puts their data into action to generate insight, and AI Networking pushes the potential for performance. This is the foundation for the next generation of reasoning infrastructure and competitive advantage."
Qumulo Helios AI Agent
Helios, Qumulo's AI agent, integrates system-wide telemetry to provide a self-managing, self-diagnosing, and self-optimizing data environment. Telemetry collects and analyzes data from remote sources to gain insights about a system's performance and help discover areas to improve. Built into Qumulo's Data Operating System, Helios continuously analyzes billions of operational events per day across hybrid and multi-cloud infrastructures. It identifies emerging anomalies, predicts capacity or performance issues before they occur, and automatically generates prescriptive recommendations or remediation workflows.
Helios belongs to a new type of machine-coordinated platform intelligence for unstructured data. It binds telemetry from compute, storage, cloud and network layers into a unified model that enables reasoning proactively. The result is a system that automates troubleshooting and debugging and anticipates architectural and workload challenges, helping enterprises stay ahead of potential disruptions. With support for MCP – the open-source standard for connecting AI applications to external systems - Helios extends its reach into Qumulo's partner ecosystem, allowing external agents and orchestration frameworks to participate in the same reasoning fabric, creating an autonomous data platform.
Qumulo Cloud AI Accelerator
The Qumulo Cloud AI Accelerator bridges the company's Cloud Data Fabric with public cloud compute environments to create a smooth path for AI and analytics workloads. Taking advantage of Qumulo's NeuralCache technology, it makes intelligent, predictive prefetching and accelerated data streaming possible between on-premises and public cloud environments. Whether data originates in an enterprise data centre, a sovereign edge enclave or a cloud region, Cloud AI Accelerator allows data to move only when and where it is needed – with minimal latency and no manual orchestration.
NeuralCache is integrated into Qumulo’s Cloud Data Fabric and uses various AI and machine learning models to dynamically optimize read/write caching, aiming to improve efficiency and scalability across cloud and on-premises environments.
Through the new Cloud AI Accelerator, users can connect exabyte-scale datasets to hyperscale compute resources from AWS, Azure, Google Cloud, Oracle Cloud Infrastructure or newer AI service providers. Cloud AI Accelerator optimizes data pathways dynamically so that hot datasets for AI training, inference or rendering are delivered to the right accelerator clusters at the right time. In effect, it changes the economics of AI, eliminating the duplication and idle replication that can characterise cloud-based AI architectures.
Qumulo AI Networking
Qumulo AI Networking is a set ofnew high-performance data movement protocols optimized for GPU-based AI training and inference environments. Qumulo now natively supports RDMA (remote direct memory access), RDMA over Converged Ethernet v2 (RoCEv2), and NFS over RDMA, with S3 over RDMA in active development. Because RDMA allows access from the memory of one computer into another directly, without involving either operating system, the new transport protocols open near-memory bandwidth between storage and accelerated compute clusters, reducing latency and CPU overhead for large-scale AI operations.
By extending the Qumulo Cloud Data Fabric with these high-speed, low-latency data paths, Qumulo enables integration with NVIDIA DGX, AMD Instinct and other compute infrastructures featuring GPUs. The result is extreme throughput for model training, simulation and AI reasoning workloads, bringing the data fabric as close as possible to the computational fabric. Qumulo AI Networking unifies the traditionally separated worlds of storage and compute into a single, coherent performance domain, accelerating innovation across (for example) scientific research, media production, genomics and industrial design.
Qumulo's new capabilities are available in preview stage starting today, with general availability across the coming months. Demonstrations are available at SC25 where Qumulo’s engineers can advise on HPC and AI workflow. qumulo.com
