cloud ai

Breaks down running AI on Linux locally versus in the cloud: when to choose each, trade-offs in latency, privacy, offline use, scalability, and cost. Details GPU/driver needs (CUDA/ROCm), frameworks and runtimes, containers, and model sizing. Includes guidance for LLM inference, vision, and fine-tuning, plus hybrid workflows, cost modeling, and security/compliance considerations to help pick the right stack.