As LLMs move into production, inference has become a systems engineering problem rather than a modeling challenge. While vLLM enables high-throughput serving via PagedAttention and continuous batching, operating it reliably at scale exposes new bottlenecks. This talk explores real-world failure modes in production vLLM deployments, including tail-latency spikes, KV-cache fragmentation, and GPU under-utilization driven by memory bandwidth pressure. We walk through the inference request lifecycle, highlight where latency hides, and introduce vLLM-specific observability signals that traditional metrics miss. Attendees will gain a systems-level mental model and practical guidance for building reliable, high-performance LLM serving platforms with PyTorch.