*Results may vary by configuration and workload.
Supply GPU clusters with high-speed data access for demanding training, checkpointing, inference and HPC workloads.
Use distributed metadata and parallel I/O processing to accelerate workloads involving millions of files.
Distribute concurrent I/O across the appliance cluster to reduce traffic hotspots when many compute nodes access the same data.
Start with a three-node appliance cluster and expand capacity and performance as datasets and workload requirements grow.
Deploy TuringData Flash with a three-node starting configuration, then expand capacity and performance as your AI workloads grow.
Supports NVIDIA GPUDirect® Storage, achieving high performance to meet AI workload requirements.
Integrates multiple network planes within storage clusters to accelerate both AI model training and inference.
Supports multi-tenant deployments with per-directory QoS and quota management, ensuring each user or team receives predictable performance and fair resource allocation.
A non-volatile, distributed metadata architecture ensures superior resilience, scalability, and performance.
Enables real-time read/write access to data across edge, core, and cloud with a unified global namespace.
Seamlessly integrates object storage and file systems, enabling data movement driven by business needs and efficient management.
Setting a New Benchmark for
Performance and Cost Efficiency
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