Open Source LLMs: Systems Architecture, Efficient Fine-Tuning, and High-Performance Scaling at Enterprise Scale

Open Source LLMs is a production-focused engineering manual for developers, ML engineers, platform teams, and technical leaders who want to design, fine-tune, deploy, scale, secure, and optimize open source large language models in real enterprise environments.This is not a beginner’s guide.This is not a prompt engineering book.This is not a collection of tutorials.This book shows you how to build and operate an enterprise-grade LLM platform.What This Book TeachesYou will learn how to:Architect open source LLM systems for real production workloadsModel GPU memory usage, KV cache growth, and token throughputOptimize latency (TTFB, p95, p99) and eliminate tail bottlenecksDeploy high-performance inference engines at scaleImplement dynamic batching and multi-GPU parallelismFine-tune efficiently using LoRA, QLoRA, PEFT, and alignment strategiesModel cost per token and forecast GPU-hour consumptionDesign RAG systems that scale without exploding context costSecure LLM platforms against prompt injection and data leakageImplement multi-tenant isolation and SLA enforcementBuild observability pipelines for token-level telemetryMigrate from closed APIs to fully controlled open infrastructureDeploy in hybrid cloud and regulated on-prem environmentsBuilt Around a Real Enterprise PlatformUnlike fragmented AI books, this guide evolves one cohesive system throughout:Each chapter upgrades it:From model internals to distributed scalingFrom fine-tuning to production hardeningFrom inference optimization to governance and complianceFrom cost modeling to executive communication frameworksYou won’t just learn theory.You will design a real production blueprint.Deep Engineering FocusThis book goes beyond surface-level explanations.It includes:GPU memory math for 7B to 70B modelsKV cache scaling lawsTokens-per-second modelingCost-per-million-token forecastingFailure case studies from real production patternsPerformance regression methodologiesHardware accelerator considerationsHybrid cloud architecture designIf you are building AI systems that must handle:Thousands of concurrent usersStrict compliance requirementsMulti-region deploymentsEnterprise security standardsThis book was written for you.Who This Book Is ForML Engineers deploying open modelsPlatform Engineers building internal AI platformsDevOps and MLOps p Read more