A media company is developing an AI platform for video content analysis that requires storing...

While object storage is excellent for massive scale and metadata, NVIDIA AI infrastructure best practices for training workloads—especially video analysis—heavily prioritizeParallel File Systems (PFS). Modern AI frameworks (PyTorch, TensorFlow) and NVIDIA’s own SDKs (like DeepStream or NeMo) are optimized to read from POSIX-compliant file systems. For video content analysis, the training process involves "sharding" large video files and performing random-access reads across a massive dataset. A high-performance file system (such as Lustre, Weka, or IBM Storage Scale) provides the high throughput and low-latency metadata operations required to keep 8 or more H100 GPUs per node saturated with data. File storage allows for the hierarchical organization that data scientists use to manage datasets (e.g., /datasets/train/videos/) and supportsGPUDirect Storage (GDS), which allows the GPU to pull data directly from the storage fabric into GPU memory, bypassing the CPU to maximize ingestion throughput.