AI Memory Infrastructure

The emerging tier of persistent, object-storage-backed memory architecture sitting between GPU HBM and cold S3 — the substrate that turns stateless LLMs into stateful, multi-agent systems. Spans hot memory (GPU SRAM / HBM3e), warm memory (CPU DRAM / CXL pools), persistent context (Tier 3.5: NVMe / DPU-attached flash for "instant resume" agentic state), and the cold semantic base (S3-compatible storage for episodic, semantic, and procedural memory).

As LLMs transition from single-turn inference engines to stateful agents operating over long horizons, the surrounding infrastructure must solve the **memory wall** and the **context bottleneck**. KV-cache persistence, temporal memory graphs, and checkpoint persistence each demand a different point in the memory hierarchy — none of them fit cleanly into either GPU RAM or a flat S3 bucket. AI Memory Infrastructure names the layered architecture that bridges them.

KV-cache offloading to S3 (LMCache, SGLang RadixAttention), agent episodic memory (Mem0, Zep, Graphiti), checkpoint persistence for training and inference, multi-host KV-cache sharing via CXL, temporal knowledge graphs with `valid_at`/`invalid_at` semantics, "instant resume" agentic state.