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May 20, 20260 citationsOpen Access

RT-Core Accelerated Semantic Memory Retrieval for Local AI Systems

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MMMohamed Mahmoud Ahmed Mohamed

Key Points

  • The study aims to improve the efficiency of semantic memory retrieval using advanced GPU techniques.
  • Projected high-dimensional embeddings into 3D geometric space.
  • Utilized BVH traversal via idle RT Cores in consumer GPUs.
  • Tested on RTX 3070 using CPU KDTree as RT Core proxy.
  • Achieved 210x average speedup over linear semantic memory search.
  • Initial recall of 48% at top 10 improved to near 100% with re-ranking step.
  • Estimated potential speedup of 1,000x-3,000x with true RT Core traversal.

Abstract

We demonstrate 210x average speedup over linear semantic memory search by projecting high-dimensional embeddings into 3D geometric space and using BVH traversal — the algorithm executed by idle RT Cores in consumer GPUs. Tested on real sentence embeddings with 48% Recall@10, improving to near-100% with a re-ranking step. Results obtained on an RTX 3070 using CPU KDTree as RT Core proxy. Conservative hardware estimates suggest 1,000x-3,000x speedup with true RT Core traversal.

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Mohamed Mahmoud Ahmed Mohamed (2026) studied this question.

synapsesocial.com/papers/6a0d50f3f03e14405aa9d14ahttps://doi.org/10.5281/zenodo.20263093
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