AI teams are increasingly turning to serverless GPU platforms to manage the challenges of accessing powerful GPUs globally without the operational and financial burdens of traditional infrastructure. These platforms offer on-demand access to GPUs, automatically handling container orchestration, scaling, load balancing, and fault tolerance, charging only for actual compute time. This model is particularly beneficial for AI workloads that are bursty and unpredictable, such as model inference, batch jobs, and experimentation, where traditional infrastructure often results in idle resources and inefficiencies. Serverless GPU platforms draw from multiple providers and regions to ensure availability, performance, and compliance with data residency laws, addressing the difficulties of sourcing high-demand chips like H100s or H200s. Key factors in evaluating these platforms include cold start performance, compute variety, workload flexibility, multi-region deployment, and security compliance, with pricing models typically based on per-second usage. Providers vary in their offerings, with Cerebrium and RunPod noted for competitive pricing and performance, while Google Cloud Run offers extensive global reach. As AI adoption grows, serverless GPU infrastructure is emerging as a crucial solution for efficiently and securely scaling AI workloads globally.