Buying more GPUs does not guarantee more useful compute. Between every dataset and accelerator sits a storage path. If that path cannot sustain high throughput, respond in microseconds and serve thousands of clients at once, the GPUs wait, however powerful they are.
The cost appears across the AI lifecycle: slower checkpointing during training, longer model loads for inference, weaker retrieval-augmented generation (RAG) pipelines and lower effective GPU utilization. Radiant addresses the data path as part of the AI factory itself. Compute, networking and storage are designed and operated together, giving you a data layer built for sustained performance, live expansion and powerful control.
Give GPUs the Shortest Path to Data
High-performance media and networks only deliver their full value when the client path is equally efficient. NVIDIA GPUDirect Storage enables data to move directly between storage and GPU memory, avoiding the conventional detour through CPU memory. Fewer data copies reduce latency and CPU overhead, helping accelerators spend more time processing data and less time waiting for it.
Radiant Managed Storage supports GPUDirect-compatible data paths and RDMA-based storage connectivity. Where RDMA is used, authorization keys protect memory for local and remote access, preserving the performance of direct data movement while enforcing the boundaries required in shared infrastructure.
The result is read and write latencies that are just a few microseconds, enabling you to support interactive workloads and streaming data pipelines smoothly. Whether your AI is training on millions of small images or serving models at Internet scale, near-immediate data access keeps your workflows snappy and efficient.
Scale Without Limits
At the core of AI workloads is the necessity to ingest massive, petabyte-scale datasets without performance degradation across thousands of concurrent nodes.
- Petabyte-Scale Footprint: Radiant supports filesystems that can start at 1 PiB and seamlessly expand to several PiB as the cluster grows.
- Fungible & Flexible Allocation: Multi-tenant environments can provision multiple independent filesystems within total allocated capacity, down to 50 TiB minimum sizes.
- Live Dynamic Expansion: Filesystems expand on the fly across capacity, inode count, IOPS, and metadata performance to keep pace with growing data requirements.
- Parallel Multi-Path Engine: Designed to support thousands of simultaneous GPU client connections while consistently sustaining requested IOPS and bandwidth targets.
Achieve Blistering Speed: Millions of IOPS and Massive Throughput
Throughput is the volume of data a system can move and IOPS is how many operations it can service, and AI demands both from opposite ends of the workload: training leans on throughput to stream datasets and checkpoints fast enough to keep the GPUs fed, while distributed training and inference lean on IOPS as they fire millions of small, random reads across thousands of clients at once, which means a storage layer that falls short on either one will stall the run regardless of how strong it looks on the other. Radiant’s storage architecture supports millions of reads and writes per second alongside several gigabytes per second of sustained throughput on NVMe-backed parallel filesystems.
Use One Data Layer Across Every AI Workflow
AI workflows rarely use one access method from beginning to end. Data teams may ingest datasets through an S3 object interface, ML teams may train through a POSIX filesystem and enterprise applications may consume results over NFS or SMB.
Multi-protocol access removes the need to maintain separate file and object silos. A dataset introduced through one interface can remain available through another without an intermediate copy or conversion pipeline. That reduces duplication, preserves one current version of the data and makes new applications easier to onboard as the AI platform evolves.
Radiant provides multi-protocol storage that supports interfaces such as POSIX , NFS, SMB and S3 concurrently.
Protect Data Without Interruptions
AI environments often contain sensitive, regulated, or proprietary data. Radiant protects this data across the storage environment without slowing down access.
- Encryption at rest and in transit: Protect data in storage, between clients and storage, and as it moves between nodes.
- Centralized key management: Manage access, key rotation, and security policies from one place, with separate keys for individual filesystems or tenants.
- Space-efficient snapshots: Create point-in-time copies to recover from accidental deletion, corruption, or unsuccessful data changes.
- Writable clones: Create virtual copies of datasets for testing and model comparisons without duplicating the full dataset.
- Snap-to-object workflows: Move snapshot data to object storage for added protection and portability.
- High availability: Protects access when individual backend components fail.
- Client multipathing: Enables connections to multiple server nodes in parallel. This balances traffic across multiple IPs, improves throughput through parallel data paths, and uses multiple client-side NICs to maximize available bandwidth.
Storage Optionality to Fit Your Workload
Storage architecture can be tailored to the needs of each workload. Radiant supports leading options, including Weka, VAST Data, DDN, and Pure Storage, allowing enterprises to choose the right balance of performance, scale, and resilience.
Radiant integrates the selected platform with compute, networking, and operations. This gives you greater choice without changing the rest of their infrastructure stack or compromising on performance needs.
Govern Storage at the Level Your Business Operates
Managing storage at scale means controlling how capacity is used across users, groups, projects, and workloads. Radiant helps teams share storage securely while protecting capacity and maintaining performance.
- Quotas and limits: Set quotas for specific workloads and volumes, with soft and hard limits based on user, group, project, or directory.
- Root-squash controls: Adjust administrative access to shared filesystems as security requirements change.
- File locking: Coordinate access when multiple applications or users work with the same data.
- Activity Auditing: Record who creates, renames, or deletes files and directories.
These controls help operators investigate incidents, understand changes, and meet governance requirements.
Unlock the Full Value of Your Compute with AI-Optimized Storage
The economics of AI infrastructure are unforgiving. Every stalled data path can translate into idle accelerators, delayed jobs and lower effective utilization. Radiant treats storage as a first-class part of the AI factory, with throughput, latency, expansion, isolation, protection and maintenance engineered into the platform from the outset. Build your AI factory on storage engineered for sustained performance, sovereign control and continuous access at hyperscale.
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