This topic covers the practical tradeoffs and capabilities of leading cloud GPU and AI infrastructure providers for model training and inference as of 2026. It explains how hardware vendors (NVIDIA) and cloud providers (AWS, CoreWeave, Unicorne) differ in performance, software stacks, pricing models, and deployment patterns—information teams need to pick infrastructure that matches model size, latency and cost targets, and data governance requirements. NVIDIA remains central for high‑performance training and inference through its H100/A100 family and software ecosystem (CUDA, cuDNN, Triton), while AWS complements NVIDIA hardware with a broad accelerator portfolio (EC2 GPU instances, Trainium and Inferentia chips) plus managed services such as SageMaker. CoreWeave exemplifies GPU‑native cloud providers that prioritize flexible, on‑demand GPU capacity and competitive pricing for large‑scale training and inference. Unicorne represents smaller or emerging specialized providers and marketplaces that focus on managed GPU clusters, spot capacity, and decentralized access patterns—useful where cost, locality, or data sovereignty drive decisions. The overview also links infrastructure choices to AI data platforms and orchestration tooling: frameworks like LangChain and LlamaIndex are commonly used to build retrieval‑augmented generation (RAG) pipelines and agents that rely on consistent inference latency and colocated data. Developer tools such as GitHub Copilot, Amazon CodeWhisperer (Amazon Q Developer), and Tabnine illustrate downstream services whose performance and cost depend directly on infrastructure selection. Key trends for 2026 include hardware specialization, model quantization and mixed‑precision optimization, spot/marketplace capacity for cost savings, and an increasing emphasis on decentralized and data‑aware deployment strategies. The result: pick infrastructure based on workload profile, integration needs (RAG, agent frameworks), and governance constraints rather than on headline performance alone.