Inference engineers and AI infrastructure architects deploying large-scale agentic AI on NVIDIA GPU clusters who need to evaluate the performance economics of KV cache tiering for hybrid Mamba-Transformer models.
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Agentic inference is constrained by context memory before it is constrained by compute. When concurrent sessions exceed what GPU HBM can retain, every human-scale pause becomes an eviction and every returning turn pays full prefill cost on a prefix the engine already produced, competing for the same resources as every other active session.
Hybrid Mamba-Transformer architectures appear to have small KV footprints when sized from attention-head arithmetic alone, but enabling prefix caching requires checkpointing recurrent state alongside attention KV, collapsing usable capacity far earlier than the attention layer count suggests and moving the eviction cliff to roughly the same concurrency as a dense 70B model.
A DRAM-only cache resolves the eviction cliff at moderate concurrency but saturates as load grows, while a hierarchical DRAM-plus-NVMe tier keeps scaling. The depth of the hierarchy determines where the serving frontier moves, not the size of any single cache layer. Below the eviction cliff, a KV tier adds write-through overhead rather than benefit, making tier deployment a concurrency-regime decision rather than a universal optimization.
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