High-Performance Storage (HPS) & Parallel File Systems
Stop starving your GPUs. Traditional NAS and SAN storage choke under the massive random I/O demands of modern AI training and high-performance computing. We architect POSIX-compliant, distributed parallel file systems and NVMe-oF fabrics that saturate NVIDIA GPUDirect Storage (GDS) channels at 100+ GB/s.
Enterprise Use Cases & Workloads
GPU Training
Feeding 8-GPU HGX Training Supernodes
Deliver multi-terabyte datasets (images, tokens, video frames) instantaneously to multi-node GPU clusters, ensuring your H100, H200, and B200 supernodes never sit idle waiting for data.
LLM Checkpoints
Rapid LLM Checkpointing & State Recovery
Instantly serialize and save massive multi-billion parameter model checkpoints during long training runs, slashing checkpoint I/O pause times from minutes down to seconds.
Life Sciences
Cryo-EM Microscopy & Genomics Ingestion
Ingest, preprocess, and analyze multi-terabyte raw movie files from electron microscopes and high-throughput sequencing pipelines without metadata bottlenecks.
HPC Simulation
Multi-Node CFD & Weather Simulation Checkpoints
Stream massive floating-point datasets and restart files across hundreds of distributed compute nodes executing Ansys, OpenFOAM, or weather modeling codes.
Ecosystem & Turnkey Integration Support
We Validate the End-to-End I/O Pipeline. High-performance storage requires precise tuning across client kernel drivers, file system block sizes, and network congestion parameters. Kirti Telnet delivers fully benchmarked, turnkey storage fabrics.
Parallel File Systems & SDS
Best for: Distributed POSIX High-Throughput Storage
NVMe Storage Arrays & JBOFs
Best for: Dense All-Flash NVMe Raw Capacity
Network Fabrics & Acceleration
Best for: Ultra-Low Latency Kernel-Bypass Data Paths
Storage Management & Telemetry
Best for: Automated Tiering & Health Monitoring
High-Performance Storage Reference Architectures
High-Performance Storage (HPS) disaggregates metadata handling from bulk data streaming to eliminate bottlenecks. Below are our core enterprise HPS building blocks.
Metadata Acceleration Node
File System Control & Small-File Indexing
Form Factor: 1U Rackmount (Redundant Titanium PSU, High-Endurance NVMe)
Processor: Dual AMD EPYC 9004 Series | 512 GB to 1 TB ECC RAM
Storage: Ultra-Low-Latency Gen5 NVMe (RAID-1 Metadata Pool)
Network: Dual 200G/400G Adapters
Starting Price
~₹ 14,50,000
Per Node
Bulk Capacity Storage Node
High-Throughput Data Streaming Pool
Form Factor: 2U / 4U Rackmount (High-Density Drive Trays)
Processor: Single / Dual AMD EPYC 9004 Series | 256 GB ECC RAM
Storage: 24x to 36x Enterprise U.2/E3.S NVMe Drives (Erasure Coded Pool)
Network: Dual 100G/200G NICs
Starting Price
~₹ 28,00,000
Per Node
High-Density NVMe JBOF Array
Direct Attached Storage Expansion
Form Factor: 2U Rackmount (Dual Controller Expansion Shelf)
Controller: Hardware Enclosure Management (SES-2) & Redundant Power
Storage: 24x Dual-Port U.2 NVMe Hot-Swap Bays
Network: 100G/200G NVMe-oF Connectors
Starting Price
~₹ 18,50,000
Per Array
Core High-Speed Fabric Switch
Low-Latency Interconnect Backbone
Form Factor: 1U / 2U Rackmount (Non-Blocking ASIC Switch)
Architecture: Non-blocking enterprise switching with hardware adaptive routing
Ports: 32x to 64x Ports of 400Gb/s InfiniBand or 200Gb/s RoCEv2
Use Case: Lossless RDMA fabric for parallel storage clusters
Starting Price
~₹ 12,00,000+
Per Switch
Architectural Deep Dive
Bypassing the CPU via NVIDIA GPUDirect Storage (GDS)
In a traditional architecture, when a GPU needs data from storage, the data must travel from the NVMe drive through the network card into system RAM (CPU memory), before being copied over PCIe into GPU VRAM. This creates a severe CPU bottleneck. GPUDirect Storage establishes a direct DMA pathway between the NVMe storage fabric and GPU VRAM, bypassing the host CPU completely, reducing I/O latency by up to 70%, and unlocking massive multi-gigabyte-per-second throughput.
Distributed Metadata Architecture
Traditional Network Attached Storage (NAS) stores metadata on a single controller, which instantly chokes when millions of small files are accessed simultaneously. High-Performance Storage (HPS) uses a distributed parallel architecture where metadata is striped across multiple dedicated metadata servers (MDS) and NVMe pools, allowing thousands of compute nodes to open and read files concurrently without queuing delays.
Lossless InfiniBand & RoCEv2 Fabric Tuning
Parallel storage relies on heavy striping across multiple storage targets. If even a single network packet is dropped due to buffer congestion, storage throughput plummets. We engineer our HPS networks with strict lossless parameters — utilizing Priority Flow Control (PFC), Explicit Congestion Notification (ECN), and adaptive routing on NVIDIA Quantum-2 InfiniBand or RoCEv2 switches to guarantee deterministic wire speed.
Frequently Asked Questions
Why can’t we use a standard enterprise NAS or SAN for AI training workloads?
Standard enterprise NAS and SAN systems are optimized for block-level database transactions or general office file sharing, typically topping out around 10–15 GB/s. AI training and HPC simulations require streaming massive datasets to dozens of GPUs simultaneously, demanding 50 to 200+ GB/s of continuous bandwidth. Parallel file systems (HPS) stripe data across multiple storage servers simultaneously to achieve these extreme speeds.
What is NVIDIA GPUDirect Storage (GDS) and why is it critical for HPS?
GPUDirect Storage is a direct data path between local or network-attached NVMe storage and GPU memory. By eliminating the CPU bounce buffer in system RAM, GDS cuts I/O latency, reduces CPU utilization to near zero, and ensures your GPUs spend their time computing neural networks rather than waiting for data.
How does HPS handle small-file bottlenecks (e.g., millions of 4KB images)?
Small files traditionally destroy storage performance because each file requires a metadata lookup. HPS uses distributed metadata servers paired with ultra-low-latency Gen5 NVMe namespace caching, aggregating small files into larger streams and caching metadata in RAM to maintain maximum IOPS.
Can HPS scale capacity and bandwidth independently?
Yes. Depending on the parallel file system architecture (such as WEKA or Lustre), you can scale bandwidth by adding compute/storage nodes, or expand raw capacity by attaching high-density NVMe JBOF enclosures, ensuring your storage grows dynamically with your AI models.
Request a High-Performance Storage Sizing
Whether you’re feeding 8-GPU HGX supernodes, checkpointing multi-billion parameter LLMs, or ingesting Cryo-EM raw data, our HPS architects will design the exact parallel storage fabric for your workloads.
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