Kv-Cache Management

LLM-driven agentic applications automate complex, multi-step tasks, but serving them efficiently remains difficult due to heterogeneous components, dynamic model-driven control flow, long-lived state, and highly variable latencies. Nalar is a serving framework for agent workflows that separates workflow specification from execution while providing the runtime visibility and control needed for robust performance. Nalar preserves ordinary Python interfaces and control flow through lightweight auto-generated stubs that turn agent and tool invocations into futures carrying dependency and execution-context metadata. A two-level control architecture combines global policy computation with local event-driven enforcement to support adaptive routing, scheduling, and resource management across evolving workflows. A workflow-aware KV-cache layer enables the runtime to manage cache placement and lifetime. Together, these mechanisms enable scalable, efficient, policy-driven serving of heterogeneous agentic applications without burdening developers with orchestration logic. Across three agentic workloads, Nalar reduces tail latency by 34-74% and achieves up to 3.38x speedups.

Decoding throughput improvements from larger inference batches are limited by GPU memory, which is largely consumed by the key-value (KV) cache. Prior training-free KV cache offloading alleviates this by keeping redundant context on the CPU and fetching only a sparse subset for attention, but it often degrades long-generation quality due to training-inference mismatch on sparse patterns. Meanwhile, trainable sparse attention is incompatible with efficient offloading, as unconstrained KV accesses may force large CPU-to-GPU transfers and erase throughput gains. To this end, we propose NOSA, a trainable sparse attention mechanism natively designed for KV cache offloading. NOSA explicitly constrains the volume of CPU-GPU KV transfers, thereby achieving low communication overhead and high decoding throughput. We further build NOSI, a KV cache offloading inference system that fully unlocks NOSA's efficiency. Empirical results on 1,3,8B LLMs demonstrate that NOSA outperforms KV cache offloading baselines on general, long-input, and long-generation tasks, while boosting decoding throughput by up to 5.04x, 1.92x, and 1.83x over FullAttn, InfLLMv2, and ShadowKV, respectively. We release our code at https://github.com/thunlp/NOSA.

We study offline scheduling for large language model (LLM) serving under a fixed KV-cache memory budget, where requests have heterogeneous prompt (prefill) and response (decode) lengths. Given a backlog of requests available at time zero, the scheduler forms mixed prefill/decode batches over time to minimize total end-to-end latency. We show that heterogeneity in prompt lengths fundamentally changes the problem: minimizing total latency is NP-hard, and standard policies that prioritize short outputs or small total sequence sizes can have unbounded approximation ratios. We propose Sorted-F, which repeatedly selects feasible batches using an F-metric that balances batch cardinality against downstream decode cost. With exact batch selection, Sorted-F achieves a constant-factor approximation guarantee in the unit-time, uninterrupted-decoding model with known output lengths; the guarantee also holds under a static peak-memory batch constraint. We develop an exact pseudopolynomial dynamic program for this static subproblem, scalable local-search and greedy heuristics, LP-guided variants, and a receding-horizon online extension. Experiments on public conversational and long-document summarization workloads show that F-metric-based scheduling substantially reduces latency relative to standard baselines and remains close to the LP relaxation lower bound on tractable instances.