# What is AI Infrastructure? Definition & Technical Mechanics

> **Source:** [SPIDITS AI AI Glossary](https://spidits.com/ai-glossary)  
> **Category:** Hardware & Infrastructure  
> **Canonical Citation:** [https://spidits.com/ai-glossary/ai-infrastructure](https://spidits.com/ai-glossary/ai-infrastructure)

## Definition
AI Infrastructure refers to the hardware compute, vector databases, network fabrics, orchestration layers, and MLOps platforms required to train, evaluate, and serve AI models at scale.

## Architectural Mechanics & Deep Dive
AI Infrastructure forms the foundational backbone of the modern artificial intelligence economy. From specialized silicon like GPUs and TPUs to high-speed NVLink interconnects and distributed vector databases, robust AI infrastructure enables organizations to reliably train and serve trillion-parameter models.

## Industry Applications & Real-World Use Cases
Datacenter GPU cluster management, vector indexing, distributed training, low-latency inference serving, and model monitoring.

## Frequently Asked Questions

### What are the main layers of AI infrastructure?
Silicon/Hardware (Nvidia GPUs, TPUs, LPUs), Cloud Platforms (Azure, AWS, GCP, Lambda), Data/Storage (Vector DBs, S3), and Serving Frameworks (vLLM, TensorRT-LLM).

### Why is AI infrastructure scaling critical?
Modern LLM training and high-throughput inference require immense memory bandwidth, petabit networking, and optimized software stacks to operate reliably.

## Related Terminology
- [gpu](https://spidits.com/ai-glossary/gpu)
- [inference](https://spidits.com/ai-glossary/inference)
- [vector-database](https://spidits.com/ai-glossary/vector-database)

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*From the SPIDITS AI Knowledge Base (336+ terms indexed). Explore full technical definitions: [https://spidits.com/ai-glossary](https://spidits.com/ai-glossary)*