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GPU-Resident Top-K for Agentic RAG: I Built a CUDA Kernel So My Retrieval Step Would Stop Bouncing Off the GPU

Towards Data Science Anubhab Banerjee June 19, 2026
GPU-Resident Top-K for Agentic RAG: I Built a CUDA Kernel So My Retrieval Step Would Stop Bouncing Off the GPU
AI Summary— plain English for professionals

# AI Speed Bump: Why Your Smart AI Assistants Keep Slowing Down If you're building AI systems that need to search through information quickly, there's an invisible bottleneck slowing things down—data is being unnecessarily shuttled back and forth between your computer's graphics processor and main processor. A developer found a way to keep all that data movement on the graphics chip itself, dramatically cutting the delays that pile up during real conversations with AI assistants. The practical result: AI systems that respond faster and more reliably when they need to look things up on the fly.

The PCIe transfer latency is silently bottlenecking your agentic inference. Here is how building a custom device-resident vector search kernel bypasses the CPU to unlock deterministic microsecond tail latencies. The post GPU-Resident Top-K for Agentic RAG: I Built a CUDA Kernel So My Retrieval Step

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