AI Training & Checkpointing Bottlenecks

Eliminate I/O bottlenecks in distributed training clusters with kernel-bypass storage.

Data Ingestion Pipelines

Frameworks ingesting massive training samples suffer from heavy disk read bandwidth competition. The overhead of traversing the kernel for continuous, random reads generates severe GPU starvation. Research shows that on fast NVMe SSDs, the Linux storage stack often fails to saturate hardware because the CPU itself becomes the primary bottleneck; bypassing it with user-space polling mechanisms drastically reduces CPU instruction costs and avoids I/O scheduler overhead.

Synchronous Checkpointing

Massive scale training runs must periodically save distributed checkpoints to persistent storage for fault tolerance. Default synchronous checkpointing stalls the entire training process, forcing GPUs to idle while master nodes flush state through the Linux VFS. Transparently routing these massive sequential writes into lock-free hardware queues slashes these checkpoint stalls.