Caching historically meant offloading relational databases by parking deserialized objects into dynamic RAM. Systems allocated a fixed memory footprint, assigned TTL expirations, and trusted the operating system's slab allocators to preserve stability. That tidy separation vanished as software teams integrated retrieval-augmented generation and streaming data directly into user-facing paths.
Modern workloads generate wildly divergent memory profiles. Traditional key-value stores read and write uniform strings, hashes, or JSON blobs over network sockets. In contrast, transformer-based intelligence platforms must retain the intermediate mathematical representations of conversational histories within scarce High Bandwidth Memory (HBM). When an AI inference engine processes queries, it stores attention tensors inside a key-value (KV) cache to avoid recalculating earlier tokens.
Because context windows extended past 128,000 tokens as standard practice by early 2026, the volume of raw floating-point data sitting in fast memory skyrocketed. A model serving concurrent users can consume dozens of gigabytes of VRAM purely for token history, leaving virtually no headroom for active compute weights. When an application overruns these memory thresholds, the operating system kernel steps in with fatal out-of-memory (OOM) termination signals, terminating containers without graceful failover.
