{"product_id":"optimizing-graphrag-throughput-on-nvidia-del-baggio-9798241501660","title":"Optimizing GraphRAG Throughput on Nvidia Blackwell NVFP4: Leveraging 4-bit floating-point precision and Neo4j index-free adjacency to accelerate multi","description":"\u003cb\u003eBreak Through the AI Memory Wall with Nvidia Blackwell and Neo4j\u003c\/b\u003e\u003cp\u003eAs enterprise AI moves into the era of \u003cb\u003eAgentic workflows\u003c\/b\u003e, traditional RAG architectures are hitting a physical ceiling. The complexity of modern data connections often leads to the \"Memory Wall\", a performance bottleneck where data movement is too slow for real-time reasoning. \u003ci\u003eOptimizing GraphRAG Throughput on Nvidia Blackwell NVFP4\u003c\/i\u003e provides the definitive technical blueprint to overcome these limits by merging cutting-edge silicon with native graph storage.\u003c\/p\u003e\u003cp\u003eThis book delivers a rigorous, deep-dive guide into the future of high-performance AI. This book isn't just about theory; it is about the physics of the GPU and the pointer-chasing mechanics of the graph engine.\u003c\/p\u003e\u003cb\u003eWhat You Will Master: \u003c\/b\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cb\u003eThe NVFP4 Revolution: \u003c\/b\u003e Learn to implement 4-bit floating-point precision to double your throughput without sacrificing semantic accuracy.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eIndex-Free Adjacency (IFA): \u003c\/b\u003e Understand why Neo4j's pointer-based navigation is the only sustainable way to handle billion-scale multi-hop queries.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eBlackwell Hardware Acceleration: \u003c\/b\u003e Offload complex graph traversals to Second-Generation Transformer Engines and Tensor Cores.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eHigh-Throughput Pipelines: \u003c\/b\u003e Build asynchronous retrieval patterns and predictive pre-fetching strategies to eliminate latency.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eContext Window Optimization: \u003c\/b\u003e Manage token pressure with compressed graph data and localized sub-graph extraction.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cb\u003eEngineered for Real-World Scale\u003c\/b\u003e\u003cp\u003eFrom \u003cb\u003eGlobal Supply Chain Visibility\u003c\/b\u003e involving over a billion relationships to \u003cb\u003eReal-Time Fraud Detection\u003c\/b\u003e in financial networks, discover how these technologies solve the most demanding industrial challenges of 2026.\u003c\/p\u003e\u003cp\u003eWhether you are a DevOps engineer, an AI architect, or a data scientist, this guide provides the configuration cheat sheets, custom CUDA kernel frameworks, and benchmarking methodologies needed to build the next generation of \u003cb\u003eAgentic GraphRAG\u003c\/b\u003e.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eStop searching for data. Start navigating intelligence.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003ci\u003eAdd to your collection and lead the 4-bit revolution today.\u003c\/i\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e del Baggio\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9798241501660\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Independently Published\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 12\/27\/2025\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 98\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 0.41lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 10.00h x 7.00w x 0.20d","brand":"del Baggio","offers":[{"title":"Paperback","offer_id":48852692926719,"sku":"9798241501660","price":25.0,"currency_code":"USD","in_stock":true}],"url":"https:\/\/www.whiterainbookhouse.com\/products\/optimizing-graphrag-throughput-on-nvidia-del-baggio-9798241501660","provider":"WR Book House","version":"1.0","type":"link"}