Rust for LLM Infrastructure Building Fast AI Pipelines High-Performance Rust Architectures for Model Serving, Retrieval, and

This is a practical, high-performance guide for engineers, quants, data teams, and founders who need to move beyond Python prototypes and ship production-grade AI. Across every chapter, you'll learn how to design ultra-low-latency pipelines, optimize memory consumption, implement blazing-fast tokenization and vector search, orchestrate distributed compute, and build inference engines that never fall over.
Inside, you'll learn how to:
* Architect end-to-end Rust-based LLM pipelines for retrieval, inference, and agents.
* Build high-throughput embeddings systems, vector search indexes, and streaming data processors.
* Optimize memory, concurrency, and GPU interfaces for maximum speed.
* Deploy Rust-backed microservices that outperform Python by orders of magnitude.
* Integrate Rust with Python, TypeScript, and C++ for hybrid AI systems.
* Construct reliable model-serving stacks that scale horizontally without failure points.
* Use Rust to harden security, sandbox agents, and enforce safety boundaries at the system level.
Ethan Crossley delivers the definitive playbook for the next era of AI infrastructure, where performance is strategy, latency is leverage, and Rust is the competitive edge.
If you're ready to engineer LLM systems that are fast, safe, and built to scale, this is the book that takes you all the way.

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