The democratization of open-source Large Language Models (LLMs) allows users to fine-tune and deploy models on local infrastructure but exposes them to a First Mile deployment landscape. Unlike black-box API consumption, the reliability of user-managed orchestration remains a critical blind spot. To bridge this gap, we conduct the first large-scale empirical study of 705 real-world failures from the open-source DeepSeek, Llama, and Qwen ecosystems. Our analysis reveals a paradigm shift: white-box orchestration relocates the reliability bottleneck from model algorithmic defects to the systemic fragility of the deployment stack. We identify three key phenomena: (1) Diagnostic Divergence: runtime crashes distinctively signal infrastructure friction, whereas incorrect functionality serves as a signature for internal tokenizer defects. (2) Systemic Homogeneity: Root causes converge across divergent series, confirming reliability barriers are inherent to the shared ecosystem rather than specific architectures. (3) Lifecycle Escalation: Barriers escalate from intrinsic configuration struggles during fine-tuning to compounded environmental incompatibilities during inference. Supported by our publicly available dataset, these insights provide actionable guidance for enhancing the reliability of the LLM landscape.

翻译：开源大型语言模型（LLMs）的民主化使用户能够在本地基础设施上微调与部署模型，但同时也使其暴露于"第一英里"部署的复杂环境。与黑盒API调用不同，用户自主管理的编排系统可靠性仍是关键盲区。为填补这一空白，我们首次对开源DeepSeek、Llama和Qwen生态系统中705个真实故障案例展开大规模实证研究。分析揭示了一个范式转变：白盒编排将可靠性瓶颈从模型算法缺陷转移至部署栈的系统性脆弱层面。我们识别出三个关键现象：（1）诊断分化：运行时崩溃明确指示基础设施摩擦，而功能错误则成为内部分词器缺陷的特征标识。（2）系统同质性：根本原因在不同系列模型中呈现收敛态势，证实可靠性障碍是共享生态系统的固有特性而非特定架构所致。（3）生命周期升级：障碍从微调阶段固有的配置冲突，逐步升级为推理阶段环境不兼容的复合问题。基于我们公开的数据集，这些发现为提升LLM生态系统的可靠性提供了可操作的指导框架。