What is AI Infrastructure?

Building AI products requires far more than just a clever model. The infrastructure underneath determines whether your system can train on enough data, serve users with low latency, and scale economically. The hardware layer dominates costs: NVIDIA H100 and B200 GPUs are the workhorses for training, with specialized inference chips like Google TPUs, AWS Trainium and Inferentia, and Groq LPUs offering alternatives optimized for specific workloads. The networking layer connects GPUs into clusters using technologies like NVLink and InfiniBand — high bandwidth interconnects matter because training a frontier model requires thousands of GPUs communicating constantly. The storage layer holds massive training datasets and model checkpoints, requiring parallel file systems that can keep up with GPU read speeds. The software layer handles distributed training (PyTorch FSDP, DeepSpeed), inference serving (vLLM, TensorRT-LLM), and orchestration (Kubernetes, Ray, SLURM). Cloud providers (AWS, GCP, Azure) and specialized GPU clouds (Lambda Labs, Modal, Together AI, CoreWeave) provide managed access to this stack. The infrastructure choices a team makes affect every aspect of their AI product: training cost, inference latency, scaling economics, and ultimately whether the business can be profitable. Infrastructure isn't glamorous, but it's where many AI startups quietly succeed or fail.