Dileeshvar Radhakrishnan

Every coding assistant, enterprise chatbot, and agent loop resends the same tool schemas, system instructions, and policy text on every turn, and the GPU rebuilds all of it into attention state before it can emit a single output token. Prompt caching computes that prefix once and reuses the KV state, and MemKV takes it from a per-process optimization to a shared NVMe-backed tier that survives routing across replicas, HBM eviction, worker restarts, and concurrency.

Cache hits are not the outcome. Part 3 turns the architecture from Parts 1 and 2 into an evaluation framework: capture an honest tail-latency baseline first, treat MinIO's published 53-second-to-703-millisecond TTFT result as a proof point to reproduce rather than a business-case input, and follow the measurement chain from repeated context through prefix reuse and avoided recompute to unit economics, ending in a buyer checklist that judges a shared context tier on P99 TTFT and jitter rather than throughput or capacity.

A prefix cache that only helps one process is useful, but requests move across replicas, HBM fills, sessions spill, and workers restart, so reuse that lives inside a single worker is not a fleet architecture. Part 2 works through what the serving stack needs once KV state leaves local GPU memory: a tier that is larger than HBM, fast enough that restore beats recompute, shared across workers, and reachable through the runtime's own KV transfer path, which is memory behavior at cluster scope rather than storage.

Agentic AI applications resend the same project rules, tool schemas, and document context on every turn, so an expensive GPU fleet spends much of its time rebuilding a prefix it has already processed. Part 1 of three reframes prompt caching as an operating-margin lever rather than a model feature, maps prompt caching, prefix caching, KV cache, and KV cache offload to the business questions each one answers, and argues that reusable context needs a memory path rather than ordinary enterprise storage.

Worker A gathers evidence, publishes an accepted handoff, starts another edit, and is deleted mid-sentence with the unfinished tail left visible. Worker B starts in a fresh runtime with no session state, verifies the last accepted boundary in the same authorized AIStor Memory Workspace, discards the unchecked tail, and continues the work rather than restarting it.

Personal AI agents become useful as they learn you, but that familiarity should not require one agent accumulating your entire life. AIStor Memory gives each agent a bounded relationship with its own learned history, active workspace, and credential scope, all under your control.

Every agent begins with the experience your organization has already earned.

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