Post-training quantization (PTQ) for multimodal large language models (MLLMs) faces a distinct optimization challenge: text and vision tokens can induce conflicting gradients when learning a shared channel-wise transformation. We identify this issue as modality gradient conflict, characterized by opposing update directions and severe magnitude imbalance between modality-specific gradients, and propose Modality-Coordinated Transformation Quantization (MCTQ) to address it. MCTQ combines whitening-based preconditioning, modality-decomposed reconstruction, and modality-coordinated optimization to learn a single shared transformation that serves both modalities without introducing modality-specific parameters or inference overhead. Across multiple MLLMs, benchmarks, and precision settings, MCTQ consistently outperforms existing channel-wise PTQ methods, with the largest gains under aggressive low-bit quantization. These results show that coordinating modality-specific gradients is critical for learning effective shared transformations in multimodal PTQ. Code and reproduction materials are available at https://github.com/jtsdw/UniMQ.