Bringing Compute to Storage for Cloud AI Workflows

Traditional cloud architectures separate compute and storage, forcing data to move across networks for AI processing. This creates bottlenecks for data-intensive workflows like model training and inference. Prabhat Sharma explores a paradigm shift: bringing computation directly to storage layers (e.g., computational storage drives, smart NICs, or in-storage processing).

Key Insights:

1. Bandwidth & Latency Challenges: Moving petabytes of data between storage and compute clusters wastes time/resources, especially for distributed training.

2. Emerging Solutions:

   • Computational storage devices with embedded processors (e.g., FPGAs, ASICs) can preprocess, filter, or transform data locally.

   • Near-storage compute (e.g., embedding GPUs in storage servers) reduces data movement.

3. AI Workflow Benefits:

   • Faster data loading/augmentation during training.

   • Lower-latency inference by processing data at the edge.

   • Reduced egress costs and network congestion.

4. Feasibility Hurdles:

   • Hardware heterogeneity and software ecosystem fragmentation.

   • Re-architecting data pipelines for distributed processing.

   • Cost trade-offs of deploying specialized hardware.

Conclusion: While integrating compute and storage shows immense promise for optimizing AI workloads, its adoption hinges on mature tooling, standardized APIs, and careful cost-benefit analysis. Early experiments suggest significant performance gains, making this a trend to watch as cloud providers and hardware vendors innovate.