AI Infrastructure
The chips, clouds, and data centers powering the AI revolution
Infrastructure is the limiting factor. Whoever controls AI compute controls AI's future. NVIDIA dominates, but challengers are emerging. Hyperscalers are spending unprecedented billions. And efficiency innovations may reshape the economics entirely.
The Infrastructure Stack
AI infrastructure spans three layers: chips (GPUs, TPUs, custom silicon), cloud platforms (where compute is accessed), and data centers (physical facilities). NVIDIA dominates the first layer; competition is fiercer at higher levels.
NVIDIA Dominance
NVIDIA's GPUs power virtually all AI training and most inference. The H100 became the most sought-after chip in history. Blackwell is the next generation.
- 80GB / 141GB HBM3 memory
- ~$25K-40K per chip (reported)
- Powers GPT-4, Claude, Gemini training
- The "gold standard" for AI training
- Up to 192GB HBM3e memory
- ~2.5x performance vs H100
- Ramping production late 2024-2025
- Already supply constrained
Chip Landscape
Challengers are emerging, but NVIDIA's CUDA ecosystem remains its deepest moat.
- 192GB HBM3 memory (more than H100)
- Competitive on some benchmarks
- ROCm software improving
- Price competitive, gaining traction
- Purpose-built for AI workloads
- Powers Gemini training
- Available via Google Cloud
- Cost-effective for certain workloads
GPU Cloud Providers
Access to compute is stratifying. Hyperscalers have the most capacity; specialists offer alternatives.
- AWS: Largest cloud, Trainium chips
- Azure: OpenAI partnership, NVIDIA focus
- GCP: TPUs + NVIDIA, Gemini home
- Best for: Enterprise, long-term contracts
- CoreWeave: NVIDIA preferred partner
- Lambda Labs: ML-focused cloud
- Together AI: Open model inference
- Best for: Startups, flexible access
The Economics
AI compute costs are falling rapidly — but demand is growing even faster.