High performance storage for HPC and AI applications
Why modern AI and HPC workloads require purpose‑built storage
AI and High‑Performance Computing (HPC) are transforming how organisations analyse data, train models and accelerate innovation. But these workloads place extreme demands on infrastructure - especially storage. Traditional storage platforms simply can’t keep up with the scale, speed and concurrency required.
Ultra‑high throughput for massive data pipelines
AI training and HPC simulations consume and generate enormous datasets that must be delivered at line‑rate to GPUs and compute nodes.
Purpose‑built parallel file systems and NVMe‑optimised architectures eliminate bottlenecks that slow down training cycles.
Sustained multi‑GB/s performance ensures GPUs stay fully utilised instead of sitting idle waiting for data.
Low latency for real‑time processing
Inference engines and real‑time analytics require microsecond‑level response times.
Specialised storage tiers (NVMe, NVRAM, burst buffers) reduce latency dramatically compared to traditional SAN/NAS.
Optimised metadata handling is essential for workloads with millions of small files.
High concurrency for GPU and cluster workloads
Thousands of compute threads may hit the storage layer simultaneously.
Parallel I/O and multi‑node access are essential to avoid contention.
Optimised read/write patterns support mixed workloads (training, inference, preprocessing, checkpointing).
Seamless integration with AI and HPC ecosystems
Compatibility with frameworks like PyTorch, TensorFlow, RAPIDS, Slurm, Kubernetes, and Spark.
Support for GPU‑direct and RDMA accelerates data movement.
APIs and automation streamline data pipelines and MLOps workflows.
Multi‑site, hybrid and cloud‑ready architectures
AI workloads often span edge, datacentre, and cloud.
Global namespace and object storage simplify data access across locations.
Cloud bursting and replication support flexible scaling.
AI and HPC demand a new class of storage
Modern AI and HPC workloads require storage that is:
Fast enough to feed GPUs
Scalable enough to handle exploding datasets
Concurrent enough for multi‑node compute
Resilient enough to protect mission‑critical data
Efficient enough to control long‑term cost
Purpose‑built storage isn’t optional… it’s foundational to achieving performance, accuracy and ROI in AI and HPC environments.
