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代理面临关键的运行时优化挑战：需要高线程数以掩盖I/O延迟，但Python的全局解释器锁（GIL）会串行化执行。我们证明，简单的线程池扩展会导致"饱和悬崖"现象：在边缘典型配置中，当过度配置线程数（N ≥ 512）时，性能下降≥20%。我们提出了一种轻量级分析工具和自适应运行时系统，使用阻塞率指标（β）来区分真实的I/O等待与GIL争用。我们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512 MB-2 GB RAM设备上受限于约8倍内存开销）和asyncio方案（在CPU密集型阶段会阻塞）。通过对七种边缘AI工作负载配置（包括使用ONNX Runtime MobileNetV2的真实ML推理）的评估，系统实现了93.9%的平均效率。与Python 3.13t（自由线程）的对比实验表明，虽然消除GIL能在多核边缘设备上实现约4倍吞吐量，但在单核设备上由于上下文切换开销，饱和悬崖现象仍然存在，这验证了我们的β指标在GIL和无GIL环境中的普适性。本研究为传统解决方案失效的内存受限边缘AI系统提供了实用的优化策略。