AWS reports that conventional prefill-decode serving transfers KV cache only from prefill to decode. On later turns, prefill may recompute KV projections for generated response tokens, and a full cache miss can require recomputing the entire conversation history. Mechanism: The described bi-directional transfer returns previously computed KV blocks from decode to prefill on subsequent turns, coordinated by a KV-cache-aware router and governed by a configurable recompute threshold. This makes cache reuse a cross-turn serving property rather than a one-way handoff.
Why now: Multi-turn agent interactions accumulate tool results and model responses, so repeated prefill work can grow alongside latency and accelerator use. AWS reports up to 1.6x lower TTFT for Qwen3-32B and up to 3.3x for GLM-5-FP8 in a 1P1D benchmark on EC2 P5en with EFA. First implication: Teams using disaggregated serving should evaluate whether reverse KV transfer reduces repeated prefill work under their actual context lengths, cache retention settings, and routing behavior.
Those figures are AWS-reported results from a particular GPU/EFA setup; the source says outcomes vary by workload and configuration, so they do not establish a general speedup across deployments. Evidence boundary: The evidence is a vendor engineering post and its described benchmarks, not an independently reproduced comparison across hardware or traffic patterns. First watch condition: Look for independent measurements across different context lengths and cache-miss rates that report both TTFT and accelerator utilization.