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arXiv 最近完成扫描：2026-11-15 06:43 · 全站最近入库：2026-11-14 15:09各信源进度 →

统计口径与数据新鲜度

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版本更新 · v2arXiv:2606.00866

Idleness is Relative: Exploiting Tool-Call Idle Windows for Offloading in Agentic Systems with MORI

Tian Xia, Yi Xu, Hanchen Li, Zhifei Li, Xiaokun Chen, Hao Kang, Yifan Qiao, Ion Stoica

原文摘要

Agentic programs alternate between LLM inference and tool calls, each request extending the previous one's context. Program-aware schedulers pin a program's KV cache in GPU HBM across tool calls to avoid re-prefilling it at every step, but as contexts grow, pinned KV cache exceeds HBM, raising program eviction: deciding when a program releases GPU memory and when it returns. Offloading to CPU DRAM lets an evicted program wait out a tool call instead of recomputing its context, but existing schedulers leave DRAM to LRU, so offloaded programs thrash. Making these decisions is hard: tool-call durations vary by orders of magnitude and are unknown in advance. We observe that programs nevertheless stay busy (short tool calls) or idle (long ones) for extended periods, and use a program's idleness, its recent fraction of time in tool calls, as a continuous, relative metric. MORI ranks programs by idleness, keeps the least idle in HBM and the next in DRAM, and drops the rest, enforcing admission control at both tiers under any GPU-to-CPU capacity ratio; it also proactively demotes idle programs to make room for ready ones. On real coding-agent traces, MORI achieves on average 64% higher throughput and 72% lower p90 TTFT than the best offloading baseline under high load.

cs.OS

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.21023

Demystifying Numerical Instability in LLM Inference: Achieving Reproducible Inference for Mission-Critical Tasks with HEAL

Zhenting Zhu, Lucas Thai, Shan Yu, Yicheng Liu, Yifan Qiao, Chenxi Wang, Harry Xu, Junyi Shu

原文摘要

As Large Language Models (LLMs) deploy into mission-critical domains (e.g., finance, medicine, and law), output reproducibility has become a strict system requirement. While practitioners use greedy decoding to eliminate algorithmic stochasticity, empirical deployments with 16-bit precisions still exhibit catastrophic output divergence across heterogeneous GPUs. Through SASS-level profiling, we reveal that this inconsistency is fundamentally driven by truncation errors introduced during downcasting at kernel boundaries. However, achieving reproducibility via a global FP32 pipeline incurs prohibitive system penalties: bypassing 16-bit hardware accelerators hurts compute efficiency, while upcasting the KV cache doubles memory overhead. To bridge this gap, we propose Hybrid Error ALleviation (HEAL), a targeted intervention that approximates FP32 precision while resolving hardware constraints through two targeted mechanisms. First, recognizing that floating-point formats underutilize their bit-width for Q, K, V tensors, HEAL applies INT16 quantization that preserves numerical stability without expanding the KV cache footprint. Second, HEAL synthesizes high-precision matrix multiplications via an algebraic error compensation strategy, executing entirely on high-throughput 16-bit Tensor Cores. To evaluate our approach practically, we introduce MCR-Bench, a benchmark targeting reproducibility in mission-critical tasks. HEAL achieves the same level of reproducibility on downstream tasks as the FP32 baseline while reducing the performance overhead by up to 6.0$\times$.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2505.04021Google Research

Prism: Cost-Efficient Multi-LLM Serving via GPU Memory Ballooning

Shan Yu, Yifan Qiao, Mingyuan Ma, Yangmin Li, Shuo Yang, Xinyuan Tong, Yang Wang, Zhiqiang Xie 等 21 位作者

原文摘要

Inference providers must maintain availability for many LLMs, including low-volume but essential models, making resource efficiency increasingly important as token prices fall. Analysis of production traces reveals a dynamic bursty-group pattern in which sets of models become active together and shift over time; existing space- and time-sharing approaches lack principled mechanisms to adapt to this variability, forcing trade-offs between SLO adherence and efficiency. We observe that elastic memory allocation can unify spatial and temporal sharing. Based on this insight, we have developed Prism, a memory-centric LLM co-serving framework that applies memory ballooning to reclaim memory across models and support both forms of sharing under a single scheme. Prism's balloon driver, referred to as kvcached, has been open-sourced at https://github.com/ovg-project/kvcached, and deployed in production environments across 10K+ GPUs.

cs.DC · cs.AI · cs.LG · cs.PF

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arXiv ↗PDF ↗