Technical Glossary
Detailed explanations of key terms in AI infrastructure, system architecture, and engineering.
Autonomous Agent
A next-generation AI system that sets goals, reasons, and completes tasks by interacting with external tools and environments on its own, without continuous human instruction.
Foundation Model
A versatile AI model trained on vast amounts of data using self-supervised learning, not limited to specific tasks, and adaptable to various downstream tasks such as language, images, and code.
Frozen v2 (Model Architecture Freezing & Stability)
An architectural design methodology that freezes and optimizes AI model hyperparameters and layer structures to maintain predictability, reproducibility, and high throughput across enterprise inference platforms.
Hardwiring (Acceleration via Dedicated Accelerators)
A technique that eliminates general-purpose CPU/GPU instruction overhead by directly implementing specific compute paths into dedicated hardware circuits like TPUs, achieving ultimate execution speed and power efficiency.
Latency (Inference Latency Reduction & Real-time Response)
The response delay from user request submission to the initial token response (TTFT) and completion of AI inference. A critical metric determining user experience and ROI in enterprise LLM deployments.
Paradigm Shift
A fundamental and dramatic change in the dominant norms, framework of thinking, or technological foundation in a certain field due to new discoveries or innovations.
Vertical Integration (In-house Integration of Chips, Infra, Models & Services)
Google's powerful architectural strategy of seamlessly designing and controlling custom AI chips (TPUs), optical interconnects, cluster orchestration, foundation models (Gemini), and end-user services in-house.