Production AI Systems, LLMOps & Model Compression

Welcome to the definitive guide to Production AI Systems, LLMOps, High-Throughput Inference Engines, and Model Compression. Deploying state-of-the-art deep learning and LLM architectures in production demands deep systems programming, operating system memory virtualization, hardware cache alignment, and numerical precision quantization.

This curriculum derives every fundamental production serving paradigm and quantization algorithm from mathematical first principles and implements each engine from scratch in both pure Python and pure Rust without third-party frameworks.

Explore lessons from the sidebar to inspect step-by-step mathematical proofs, zero-dependency implementations, interactive zoomable diagrams, and knowledge verification checkpoints.

Curriculum Architecture at a Glance#

This course provides comprehensive mathematical foundations and production-grade implementations across the production AI lifecycle:

1. High-Throughput Serving & PagedAttention Virtual Memory: Analytical KV cache memory footprints ($2 \times 2 \times n_{\text{layers}} \times d_{\text{model}} \times L_{\text{ctx}}$), internal and external memory fragmentation breakdowns, OS virtual memory page table mapping, and zero-overhead non-contiguous physical block allocation algorithms (vLLM style).
2. Precision Quantization (PTQ, QAT, AWQ & GPTQ): Uniform affine and symmetric quantization mappings ($q = \text{clip}(\text{round}(x / S) + Z)$), scale factor and zero-point derivations, Straight-Through Estimators (STE) for Quantization-Aware Training, Activation-aware Weight Quantization (AWQ) salient channel protection, and second-order Hessian inverse curvature updates for GPTQ ($H^{-1}$).
3. Comprehensive System Architecture & Technical Interview Mastery: End-to-end design patterns for low-latency batch serving engines, streaming SSE/WebSocket token dispatch, continuous batching iteration scheduling, and production architecture trade-off interview matrices.

Core Pedagogical Standard#

• Step-by-Step Algebraic Derivations: Every memory footprint equation, scale quantization formula, and Hessian curvature inversion is derived algebraically step by step.
• Zero-Dependency Implementations: Every serving block manager, quantization kernel, and memory engine includes complete, runnable implementations in pure Python (standard library only) and pure Rust (std only).
• Interactive Visual Architecture: Explore interactive flowcharts, mathematical formulas rendered with KaTeX, expandable diagrams, and knowledge verification checkpoints.

The complete course source code and implementations are open on the Source Repository.