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The LLM Engineer's Handbook: Self-Hosted AI in Production: Professional Techniques for Deploying, Customizing, and Fine-Tuning LLaMA, Mistral, and Ope
by Quill, Amaris
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Overview
Overview
Master the complete lifecycle of self-hosted large language model deployments-from infrastructure design to production operations.
In an era where data sovereignty, security compliance, and cost control are paramount, organizations are increasingly moving away from cloud-based API services toward self-hosted AI infrastructure. The LLM Engineer's Handbook is the definitive technical guide for engineers, architects, and technical leaders who need to deploy, optimize, and maintain production-grade LLM systems within their own infrastructure.
This comprehensive resource bridges the gap between theoretical AI concepts and real-world implementation, providing battle-tested strategies for running models like LLaMA, Mistral, and other open-source language models in secure, on-premises environments. Whether you're building HIPAA-compliant healthcare systems, implementing air-gapped deployments for government applications, or optimizing inference costs for high-throughput enterprise services, this book delivers the practical knowledge you need. What You'll Learn:
In an era where data sovereignty, security compliance, and cost control are paramount, organizations are increasingly moving away from cloud-based API services toward self-hosted AI infrastructure. The LLM Engineer's Handbook is the definitive technical guide for engineers, architects, and technical leaders who need to deploy, optimize, and maintain production-grade LLM systems within their own infrastructure.
This comprehensive resource bridges the gap between theoretical AI concepts and real-world implementation, providing battle-tested strategies for running models like LLaMA, Mistral, and other open-source language models in secure, on-premises environments. Whether you're building HIPAA-compliant healthcare systems, implementing air-gapped deployments for government applications, or optimizing inference costs for high-throughput enterprise services, this book delivers the practical knowledge you need. What You'll Learn:
- Infrastructure Design: Plan and build GPU clusters with optimal hardware configurations, network topologies, and cooling systems for cost-effective, high-performance deployments
- Security & Compliance: Implement enterprise-grade security frameworks including air-gapped architectures, encryption standards, and compliance tracking for GDPR, HIPAA, and SOC 2
- Model Optimization: Master quantization techniques (GPTQ, GGUF, AWQ) to reduce memory footprint while preserving model quality, and implement advanced inference optimizations like Flash Attention and speculative decoding
- Production Serving: Design robust API gateways, implement load balancing strategies, and deploy inference servers (vLLM, TGI, Triton) that scale from prototype to production
- Fine-Tuning at Scale: Apply LoRA, QLoRA, and RLHF techniques to customize models for domain-specific applications while managing distributed training infrastructure
- Advanced Architectures: Build RAG systems with vector databases, implement multi-model routing strategies, and orchestrate complex agent-based workflows
- Operations Excellence: Establish comprehensive monitoring, observability, and incident response procedures to maintain reliable production systems
- Machine learning engineers transitioning from cloud APIs to self-hosted infrastructure
- DevOps and platform engineers building AI infrastructure for their organizations
- Technical architects designing secure, compliant AI systems for regulated industries
- Data scientists seeking to understand production deployment considerations
- Engineering leaders evaluating build-vs-buy decisions for LLM capabilities
- Format: Trade Paperback
- Author: Quill, Amaris
- ISBN: 9798277720141
- Condition: New
- Dimensions: 10.00 x 0.55
- Number Of Pages: 264
- Publication Year: 2025
Language:
English