Standard quantization tools for the ONNX ecosystem inject QuantizeLinear and DequantizeLinear (QDQ) nodes into computation graphs using a local, node-by-node strategy that ignores the broader graph topology. We identify a critical consequence of this approach: by placing a DequantizeLinear node between a Convolution and its subsequent Activation (e.g., ReLU), the quantizer severs their contiguity, prevents runtime kernel fusion, and introduces redundant quantization round-trips. We prove that for activations satisfying f(αx) = αf(x) for α > 0 — including ReLU, LeakyReLU, and Clip(0, M) — the DequantizeLinear node can be safely commuted past the activation when the zero-point is zero, restoring the fusible pattern and eliminating the redundant round-trip. We implement this transformation in Kenosis, a Rust-based ONNX graph optimizer that integrates a comprehensive suite of pipeline-level quantization features, and evaluate the specific impact of this graph placement optimization against a controlled ablation using identical quantization parameters but naive QDQ placement. On stock ONNX Runtime 1.24, across three classifier architectures evaluated on a 1,000-image validation set for each classifier, fusion-aware placement achieves up to 1.49x higher throughput than naive placement (corresponding to a 33% reduction in latency) with identical weights and scales, and speedups of up to 2.42x over FP32 baselines. On MobileNetV2, naive placement yields a quantized model that is 12% slower than the FP32 baseline; fusion-aware placement restores a 25% speedup using the same quantized weights.

v2 (June 2026): Updated license information; revised Software Availability section.