动态分配精度，让智能体系统省钱提速
QuantClaw: Precision Where It Matters for OpenClaw
- 根据任务特点自动调节计算精度，轻任务用低精度，重任务保高精度
- 在GLM-5上实现最高21.4%成本降低和15.7%延迟减少
- 无需改代码，可直接接入现有系统，适合部署优化场景
自主智能体系统如OpenClaw因长上下文输入和多轮推理带来巨大效率挑战，导致真实开发中计算与资金成本过高。尽管量化是降低开销的常用方法，但其对智能体在真实场景下性能的影响尚不明确。本文分析了OpenClaw中多种复杂工作流的量化敏感性，发现精度需求高度依赖任务类型。基于此，我们提出QuantClaw——一个即插即用的精度路由插件，可动态依据任务特征分配精度。该方案将轻量任务导向低成本配置，同时为复杂任务保留高精度，从而在不增加用户负担的前提下降低计算成本并加速推理。实验表明，QuantClaw在保持或提升任务性能的同时，显著减少延迟与资源消耗；在一系列代理任务中，相较FP8基线，在GLM-5上实现最高达21.4%的成本节约和15.7%的延迟下降。结果证明，在智能体系统中将精度视为动态资源具有显著优势。
原文摘要 · Abstract (English)
Autonomous agent systems such as OpenClaw introduce significant efficiency challenges due to long-context inputs and multi-turn reasoning. This results in prohibitively high computational and monetary costs in real-world development. While quantization is a standard approach for reducing cost and latency, its impact on agent performance in realistic scenarios remains unclear. In this work, we analyze quantization sensitivity across diverse complex workflows over OpenClaw, and show that precision requirements are highly task-dependent. Based on this observation, we propose QuantClaw, a plug-and-play precision routing plugin that dynamically assigns precision according to task characteristics. QuantClaw routes lightweight tasks to lower-cost configurations while preserving higher precision for demanding workloads, saving cost and accelerating inference without increasing user complexity. Experiments show that our QuantClaw maintains or improves task performance while reducing both latency and computational cost. Across a range of agent tasks, it achieves up to 21.4% cost savings and 15.7% latency reduction on GLM-5 (FP8 baseline). These results highlight the benefit of treating precision as a dynamic resource in agent systems.
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