Key AI/ML Concepts for System Design

Essential AI Terms for System Architects

Understanding key AI concepts is becoming increasingly vital for system designers. As AI capabilities integrate more deeply into applications, architects need to grasp terms like Agentic AI, Large Reasoning Models, Vector Databases, and Retrieval Augmented Generation (RAG) to build robust and scalable intelligent systems. These concepts dictate how data is managed, how models interact with external systems, and how complex problem-solving can be automated.

• Agentic AI: An AI system that defines and executes plans to achieve a goal, utilizing tools and APIs, evaluating results, and iteratively improving outcomes. This implies complex orchestration and state management in the system architecture.
• Large Reasoning Model (LRM): Models trained to break down complex problems into smaller logical steps, performing sequential reasoning to arrive at solutions. This affects how computational resources are allocated and how intermediate states are handled.
• Vector Database: Specialized databases for storing and querying high-dimensional vectors. These are critical for semantic search, recommendation engines, and RAG architectures, enabling efficient similarity searches over embeddings.
• Retrieval Augmented Generation (RAG): A technique where a model retrieves relevant information from a knowledge base (often a vector database or other data sources) to augment its generation. This pattern emphasizes external data sources and retrieval mechanisms as core components of the AI system.

GPU Architecture for AI Workloads

GPUs are fundamental to AI/ML due to their architecture designed for massive parallelism. Understanding their high-level structure helps in designing infrastructure for training and inference. GPUs consist of multiple Streaming Multiprocessors (SMs), each acting as a mini-parallel engine. This allows thousands of threads to execute concurrently, masking memory latency by ensuring other threads can continue processing while some wait for data.

• Streaming Multiprocessors (SMs): Independent processing units, each containing CUDA Cores, a Warp Scheduler, Register Files, and L1 Cache. They operate in parallel.
• On-chip Interconnect & L2 Cache: Connects SMs and provides a shared coordination layer. L2 cache serves as an intermediate buffer before accessing global memory.
• Global Memory: High-bandwidth, high-latency memory accessed via Memory Controllers. The massive parallelism of GPUs is essential to hide this latency.