Deploying Python-based AI agents on resource-constrained edge devices presents a critical runtime optimization challenge: high thread counts are needed to mask I/O latency, yet Python's Global Interpreter Lock (GIL) serializes execution. We demonstrate that naive thread pool scaling causes a "saturation cliff": a performance degradation of >= 20% at overprovisioned thread counts (N >= 512) on edge representative configurations. We present a lightweight profiling tool and adaptive runtime system that uses a Blocking Ratio metric (beta) to distinguish genuine I/O wait from GIL contention. Our library-based solution achieves 96.5% of optimal performance without manual tuning, outperforming multiprocessing (which is limited by ~8x memory overhead on devices with 512 MB-2 GB RAM) and asyncio (which blocks during CPU bound phases). Evaluation across seven edge AI workload profiles, including real ML inference with ONNX Runtime MobileNetV2, demonstrates 93.9% average efficiency. Comparative experiments with Python 3.13t (free-threading) show that while GIL elimination enables ~4x throughput on multi-core edge devices, the saturation cliff persists on single-core devices due to context switching overhead, validating our beta metric for both GIL and no-GIL environments. This work provides a practical optimization strategy for memory-constrained edge AI systems where traditional solutions fail.

翻译：在资源受限的边缘设备上部署基于Python的AI代理面临一项关键运行时优化挑战：需要高线程数来掩盖I/O延迟，但Python的全局解释器锁（GIL）却将执行序列化。我们证明，朴素的线程池扩展会导致“饱和悬崖”现象：在典型边缘配置下，过度供给的线程数（N≥512）引发≥20%的性能降级。我们提出一种轻量级分析工具与自适应运行时系统，利用阻塞比指标（β）区分真实I/O等待与GIL争用。我们的基于库的解决方案无需手动调优即可达到最优性能的96.5%，优于多进程方案（在512MB-2GB RAM设备上受限于约8倍内存开销）和asyncio方案（在CPU密集型阶段阻塞）。在七个边缘AI工作负载配置上的评估（包括使用ONNX Runtime MobileNetV2进行的真实机器学习推理）显示平均效率达93.9%。与Python 3.13t（自由线程）的对比实验表明，虽然消除GIL可在多核边缘设备上实现约4倍吞吐量，但单核设备上因上下文切换开销导致饱和悬崖现象依然存在，从而验证了β指标在GIL与非GIL环境中的有效性。本研究为传统方案失效的内存受限边缘AI系统提供了一种实用优化策略。