Definition
AI Infrastructure is the physical and virtual foundation that supports the entire AI lifecycle. Without robust infrastructure, modern Large Language Models would be impossible to create or run.
Core Components
1. Compute (Hardware)
The "muscles" of AI. This includes specialized processors designed for high-parallelism:
- GPUs: Graphics Processing Units from NVIDIA and AMD.
- TPUs: Tensor Processing Units by Google.
- ASICs: Application-Specific Integrated Circuits tailored for specific AI workloads.
2. Networking
The "nerves" that connect processors. In an AI supercomputer, thousands of GPUs must share data instantly. Technologies like NVIDIA NVLink and InfiniBand enable the extreme speeds needed for a cluster to act as a single unit.
3. Storage
The "memory" that holds massive datasets. AI training requires feeding petabytes of data into models at high speeds, necessitating specialized high-throughput storage systems.
4. Software Stack
The "tools" used to manage the hardware. This includes libraries like CUDA, orchestration tools like Kubernetes, and AI frameworks like PyTorch and TensorFlow.
Current Trends
- Hyperscale Data Centers: Massive facilities (like those built by Microsoft, Google, and Amazon) dedicated entirely to AI.
- On-Device AI: Moving infrastructure to the edge (phones and laptops) for privacy and speed using specialized NPUs (Neural Processing Units).
- Energy Efficiency: Research into reducing the staggering environmental impact of AI compute.
Learn more about specific hardware in our entries on GPU Computing and TPU.