Phase 3: AI Infrastructure
Training a large language model isn't like running a web server. It requires thousands of specialized chips working in perfect coordination, connected by networking that makes ordinary Ethernet look like a garden hose. This phase explains the physical and logical infrastructure that makes modern AI possible — from the chips to the data center fabric.
GPU Clusters & AI Accelerators
Why GPUs dominate AI training, how NVIDIA's A100 and H100 differ, what Google's TPUs do differently, and how cloud providers rent these machines by the hour.
Start here →AI Training Infrastructure
How large models are actually trained — the data pipeline, batch processing, checkpoint storage, and the orchestration layer that keeps thousands of GPUs in sync.
Learn training infra →Distributed Computing for AI
Data parallelism, model parallelism, tensor parallelism, and pipeline parallelism — the strategies that let one model span thousands of GPUs.
Explore distributed AI →AI-Optimized Networking
InfiniBand, RoCE, and NVLink — the specialized networking technologies that move data between GPUs fast enough to keep up with trillion-parameter models.
Explore AI networking →Frequently Asked Questions
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