Redundant Context Processing Slows Multi‑LLM Pipelines Large language model pipelines increasingly chain several fine‑tuned variants derived from a common base, but each model recomputes the full context during the prefill stage, creating significant latency and throughput bottlenecks [1]. The duplicated work grows linearly with the number of variants, limiting real‑time applications that rely on rapid multi‑LLM responses [1]. Researchers identified this inefficiency as the primary motivation for a new sharing framework [1].

DroidSpeak Reuses KV‑Cache Across Related Models The system inspects the key‑value (KV) cache of the foundational model and isolates layers whose activations remain useful for downstream fine‑tuned versions [1]. For each variant, only the identified layers are recomputed, while the rest of the cache is retained, eliminating redundant computation [1]. This selective reuse targets models that share the same architecture and base weights, enabling seamless integration into existing serving stacks [1].

Selective Layer Recalculation Preserves Accuracy Experiments on diverse datasets show that the layer‑wise caching strategy incurs only a few percentage points deviation from baseline task performance [1]. Accuracy metrics remain within acceptable margins, confirming that speed gains do not come at the cost of significant quality loss [1]. The authors report that the trade‑off is consistent across multiple model pairs and tasks [1].

Benchmarks Show Up to Threefold Throughput Gains On benchmark workloads, DroidSpeak delivers up to a 3× increase in overall inference throughput compared with full recomputation [1]. Prefill latency improves on average by a factor of 2.6, accelerating the initial token generation phase that typically dominates response time [1]. The paper, authored by Shan Lu, Madan Musuvathi, and Esha Choukse, was published in Microsoft Research’s archive on May 1, 2026 [1].