Data
AI data pipelines are the infrastructure and architecture for ingesting, processing, and delivering training data to GPU clusters — ensuring GPUs are never starved of data during training, which is critical for maintaining high utilisation and economic viability.
AI training requires enormous datasets — a frontier language model may train on 10+ trillion tokens (50+ TB of text). Feeding this data to GPUs at sufficient speed is a significant infrastructure challenge: if data loading throughput is lower than GPU consumption rate, GPUs idle, training slows, and costs increase proportionally.
A single 8-GPU prior-generation GPU node can consume data at 20+ GB/s during training. If the storage system delivers only 5 GB/s, GPU utilisation drops to 25% — quadrupling training time and cost. This is why AI infrastructure must include purpose-built high-performance storage, not standard enterprise storage arrays.
An AI data pipeline includes: data ingestion (streaming or batch loading from sources), data processing (tokenisation, augmentation, shuffling), data storage (parallel file systems like Lustre, GPFS, or WEKA), and data delivery (RDMA direct-to-GPU memory, bypassing CPU). Each stage must be sized to match GPU consumption rates.
Modern pipelines also include data quality checks, deduplication, filtering, and versioning — ensuring reproducible training runs. For enterprise and sovereign AI workloads, pipelines must also enforce data access controls, audit logging, and lineage tracking for compliance.
AI storage systems must deliver 50–200+ GB/s aggregate throughput (for large clusters), sub-millisecond latency for metadata operations, and petabyte-scale capacity. Traditional NAS and SAN systems cannot meet these requirements — purpose-built parallel file systems (Lustre, GPFS, WEKA, VAST) are essential.
Constellation's facilities include high-performance parallel storage tiers integrated directly with GPU clusters via RDMA, ensuring data delivery at full GPU consumption rates without bottlenecks.
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