Mridankan Mandal, Smit Sanjay Shende
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.