AI Storage is a high-performance data architecture designed to handle the massive parallelism and low-latency requirements of AI workloads, such as model training and inference. Unlike traditional storage, it is purpose-built to eliminate bottlenecks and provide sustained throughput to GPU clusters.

What is the difference between traditional storage and AI storage?

Traditional storage is designed for sequential read/write workloads, while AI storage is built for extreme parallelism, high-throughput reads across GPUs, and sub-millisecond latency at petabyte scale.

How does storage affect GPU performance?

GPUs can only process data as fast as storage delivers it. Throughput shortfalls lead to GPU starvation, where expensive compute sits idle waiting for data.

What is the Token Warehouse in AI storage?

The Token Warehouse is a persistent storage layer for KV cache data, offloading context from GPU HBM to high-performance storage to reduce the 'prefill tax' and latency.

What is KV cache and why does it matter for AI inference?

KV cache is the stored computational state that allows LLMs to process long prompts efficiently. Proper management prevents context eviction and recomputation.

What storage infrastructure do agentic AI workloads require?

Agentic AI requires low-latency access to dynamic datasets, high-concurrency I/O for autonomous agents, and persistent context storage across sessions.