Mitigating GIL Bottlenecks in Edge AI Systems

Deploying Python based AI agents on resource-constrained edge devices presents a 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": >= 20% throughput degradation at overprovisioned thread counts (N >= 512) on edge-representative configurations. We present a lightweight profiling tool and adaptive runtime system using a Blocking Ratio metric (beta) that distinguishes genuine I/O wait from GIL contention. Our library-based solution achieves 96.5% of optimal performance without manual tuning, outperforming multiprocessing (limited by ~8x memory overhead on devices with 512 MB-2 GB RAM) and asyncio (blocked by 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, validating our beta metric for both GIL and no-GIL environments. This provides practical optimization for edge AI systems.