Achieving ultra-low latency (ULL) with end-to-end delays of 1–5 milliseconds is vital for real-time applications such as high-frequency trading, autonomous vehicles, and personalized e-commerce. This study defines latency as the time from initiating a data processing task to receiving its result, proposing a holistic approach to ULL through optimized hardware, software, and network components. Latency is broken down into network, I/O, processing, queuing, application, and security factors, reducing the standard ~19 ms latency to below 5 ms with targeted enhancements. Key strategies leverage high-performance hardware (NVMe SSDs, FPGAs, GPUs), low-latency interconnects (InfiniBand with RDMA), and efficient software. A real-time fraud detection scenario handling 10,000 concurrent queries per second is analyzed, detailing tiered technology stacks. The study contrasts networking protocols, emphasizing InfiniBand’s sub-microsecond latency advantage for ULL, and demonstrates feasibility with edge infrastructure, dedicated instances, and RDMA-enabled NVMe-oF. This framework offers practical guidance and cost estimates for 2025 ULL implementations, acknowledging that actual latency, performance, and costs may vary by use case.

Ultra Low Latency, Standard Latency, NVMe, NVMe-oF, SSD, FPGA, Low Latency Interconnect, InfiniBand, Edge Infrastructure GPU, RDMA, Optimized Hardware, Targeted optimizations, Fast API, Distributed Solution

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