Evolving Infra: Agent-Adaptive LLM Serving at Scale

Kang, Weili Xu, Ziyang Li, Zheng Du, Hanchen Li, Tian Xia, Song Bian, Tushar Krishna, Huizi Mao, Ion Stoica, Song Han

Evolving Infra: Agent-Adaptive LLM Serving at Scale: 10 upvotes on Hugging Face Daily Papers, #8 of 21 papers on 2026-11-12. Day-by-day upvote history.

Upvotes over time

It had 10 upvotes by its first day, 10 upvotes by the next day, 10 upvotes by day 3, 10 upvotes by day 7, and has 10 now. It ranked #57 of 133 papers in its week and #165 of 340 in its month. Its code repository has 12 GitHub stars.

Agentic workloads are a major source of LLM inference demand. Existing prefill--decode (PD) disaggregated serving systems offer limited flexibility for runtime adaptation of compute allocation and KV cache management, and rely on coarse signals that only partly capture the diverse and dynamic agent workload. These limitations lead to load imbalance between prefill and decode nodes and eviction of KV caches that agents will reuse. We present Evolving Infra, an agent-adaptive serving framework that adapts compute allocation to agent workloads and KV cache management to agent context management at runtime. We decouple compute allocation from key--value (KV) cache management, allowing nodes to switch efficiently between prefill and decode. Building on this design, an online planner matches the PD split to workload composition, while an agent-aware distributed KV manager guides cache eviction and transfer with signals from agent context management. Across coding, math, and computer-use tasks with GLM-5.2 and Kimi K2.6 on up to 256 GB300 GPUs, Evolving Infra reduces mean end-to-end request latency and P95 TTFT by up to 4.1x and 14x over Dynamo, NVIDIA's production framework.

Paper page on Hugging Face · arXiv

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