博客來Break Through the AI Memory Wall with Nvidia Blackwell and Neo4j
As enterprise AI moves into the era of Agentic workflows, 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. Optimizing GraphRAG Throughput on Nvidia Blackwell NVFP4 provides the definitive technical blueprint to overcome these limits by merging cutting-edge silicon with native graph storage.
This 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.
What You Will Master:- The NVFP4 Revolution: Learn to implement 4-bit floating-point precision to double your throughput without sacrificing semantic accuracy.
- Index-Free Adjacency (IFA): Understand why Neo4j’s pointer-based navigation is the only sustainable way to handle billion-scale multi-hop queries.
- Blackwell Hardware Acceleration: Offload complex graph traversals to Second-Generation Transformer Engines and Tensor Cores.
- High-Throughput Pipelines: Build asynchronous retrieval patterns and predictive pre-fetching strategies to eliminate latency.
- Context Window Optimization: Manage token pressure with compressed graph data and localized sub-graph extraction.
From Global Supply Chain Visibility involving over a billion relationships to Real-Time Fraud Detection in financial networks, discover how these technologies solve the most demanding industrial challenges of 2026.
Whether 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 Agentic GraphRAG.
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