Developer Anubhab Banerjee has introduced CUDA-TopK-Retrieval, a lightweight custom kernel designed to eliminate PCIe communication bottlenecks in agentic retrieval-augmented generation pipelines. Released as the third installment of a broader series on production-grade agentic inference, the project addresses a pervasive latency issue where standard AI frameworks offload vector similarity searches to the host CPU, forcing query embeddings and result indices to traverse the PCIe bus repeatedly. The architecture treats memory retrieval as a hardware primitive rather than a Python-based software call. Upon initialization, the entire vector corpus is uploaded once into device VRAM. During query execution, the system transfers only the embedding vector to the GPU, executes a fused dot-product scoring phase, computes local Top-K candidates via a single-thread-per-block partial selection routine, and performs a serial multi-way merge to finalize results. Only the final K indices and scores are copied back to the host. This design prioritizes code auditability and deterministic tie-breaking over algorithmic complexity, using a straightforward bubble sort per block that matches host-side CPU oracle outputs bit-for-bit. Benchmarking conducted on a legacy NVIDIA GeForce GTX 1080 using the CUDA 12.2 toolkit demonstrates substantial performance gains. Across a 45-configuration sweep spanning corpus sizes from 10,000 to 1,000,000 vectors and embedding dimensions of 384 to 1024, the GPU-resident path outperforms optimized CPU brute-force baselines by 2.43x to 8.57x at K=8, and by up to 7.76x at K=32. The speedups scale with corpus size, confirming that eliminating redundant host-to-device data transfers is the primary driver of efficiency rather than raw computational throughput. The V1 implementation carries documented limitations. At K=100, the single-lane block sorting approach becomes computationally expensive, causing the CPU baseline to surpass GPU performance in 14 of 15 test configurations. The author notes that warp-specialized tournament selection is planned for a V2 release to address this ceiling. Additionally, benchmarks were conducted without locked GPU clocks, meaning absolute millisecond values may fluctuate with thermal throttling, though structural performance ratios remain stable. Synthetic Gaussian embeddings were used to isolate architectural overhead from data distribution variables. Beyond AI inference, the project draws explicit parallels between vector retrieval and 5G New Radio beam selection, where user equipment evaluates a codebook of directional antennas and reports the strongest candidates to the baseband processor. This cross-disciplinary framing underscores the universality of high-throughput, low-latency selection primitives in distributed hardware systems. The repository provides a zero-dependency C++ implementation intended for developers seeking to optimize agent tool-call latency without introducing heavy framework dependencies. The work establishes a foundation for subsequent phases focusing on persistent agent state management and multi-agent GPU time-slicing, signaling a broader industry shift toward keeping entire reasoning loops device-resident to maximize silicon utilization.