Optimizing dedicated servers for AI inference requires a system-level approach to balance hardware resources and avoid bottlenecks in Time to First Token (TTFT), inter-token latency, and throughput. While GPUs are central, the CPU must manage tokenization, request orchestration, and RAG pipelines; enterprise-grade processors like AMD EPYC or Intel Xeon are recommended to provide sufficient PCIe lanes for high-speed interconnects. GPU requirements depend heavily on quantization: a 70B parameter model requires approximately 140GB of VRAM at FP16 precision, but only ~35GB at 4-bit quantization. However, users must allocate an additional 20% to 100% of VRAM overhead for the KV cache and context windows. System RAM should scale from 64GB for small models to over 1TB for massive multi-GPU deployments, while high-speed NVMe storage is essential to prevent loading bottlenecks. Networking needs vary from 10Gbps for standard serving to 100Gbps+ using RDMA or InfiniBand for heavy RAG workflows. For predictable, 24/7 production API serving, dedicated bare-metal infrastructure is often more cost-effective and performant than cloud instances, as it eliminates "noisy neighbor" issues and provides total control over the PCIe topology and resource allocation.