Inference needs memory: how context is becoming AI infrastructure
As enterprise AI systems evolve, the limiting factor is shifting. Model quality still matters, but it’s no longer the main issue holding systems back. Increasingly, what constrains performance, scalability, and cost is context.
Large language models are now expected to support long conversations, multi step reasoning, and complex workflows that span time, users, and systems.
Every one of those interactions generates tokens, and those tokens produce key value (KV) cache — the working memory that allows models to reason efficiently without constantly recomputing prior steps.
Most AI architectures still treat this context as temporary. KV cache typically lives in GPU memory, is tied to a single inference process, and is discarded as soon as resources are exhausted.
That approach might be acceptable for small scale experimentation, but it quickly breaks down in enterprise environments where context lengths grow, concurrency increases, and recomputation becomes expensive.
Inference context has quietly become one...
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