This paper addresses the task of generating artistic meshes with high resolution and part-level decomposition. Recent autoregressive methods generate meshes by learning the sequential distribution of mesh vertices and faces, inherently capturing strong mesh priors for high-quality topology. However, their scalability is limited by the quadratic complexity of the attention mechanism, which restricts the output resolution. To address this challenge, we introduce MeshPack, a part-wise autoregressive modeling approach that decomposes meshes into multiple part-level sequences and iteratively generates each sequence, significantly reducing computational complexity and naturally enhancing the mesh resolution. In addition, an information passing module is designed to ensure consistency across part-level sequences by fusing previously generated parts with current part, imposing global context awareness during generation. Furthermore, we introduce FurniSet3D, a high-quality furniture dataset with detailed geometry, clean topology, and semantic part decompositions for training our model. Extensive experiments demonstrate that MeshPack generates fine-resolution meshes with high topological quality and reasonable part decomposition, substantially outperforming existing methods on furniture generation.