Large language models represent a groundbreaking shift in generative AI. Yet, these advances come with a significant challenge: the high cost of model serving. To mitigate these costs, consumer-grade GPUs emerge as a more affordable alternative. This presents an opportunity for more cost-efficient LLM serving by leveraging these GPUs. However, it is non-trivial to achieve high-efficiency LLM serving on consumer-grade GPUs, mainly due to two challenges: 1) these GPUs are often deployed in limited network conditions; 2) these GPUs often exhibit heterogeneity in host systems. To address these challenges, we present MoLink, a distributed LLM serving system for large models. It incorporates several key techniques, enabling efficient LLM serving on heterogeneous and weakly connected consumer-grade GPUs. Our experiments demonstrate that it achieves throughput improvements of up to 458\% and cost-profit margin improvements of up to 151\%, compared to state-of-the-art systems. MoLink allows users on Windows, Linux, and containerized VMs to seamlessly integrate GPUs with just a few lines of code over Ethernet or public networks. Currently, it supports 18 mainstream architectures of open-source large language models. The source code is publicly available https://github.com/oldcpple/MoLink.