AI workloads present a new challenge for enterprises, ranging from compute-intensive training to lightweight inferencing and RAG referencing. The I/O profile and storage impact can vary significantly across different types of AI workload.
In a conversation with Nvidia’s Charlie Boyle, we explore the demands of checkpointing in AI, the importance of storage performance indicators like throughput and access speed, and the required storage attributes for various AI workload types. Understanding the balance between checkpoint frequency, recovery time, and risk tolerance is crucial in AI training.
The role of throughput and speed in training is closely linked, with latency adding another layer of complexity, especially in scenarios where data retrieval is involved. Similarly, fast storage and network connectivity are essential for efficient inference, ensuring quick access to enterprise data stores.
Ultimately, achieving optimal performance in AI workloads requires not only high-speed storage systems but also robust network infrastructure to facilitate seamless data access and movement. Making the right investments in technology and engineering is vital for success in AI projects.