This topic examines the practical differences and integration patterns when choosing high‑performance AI compute—from wafer‑scale accelerators (Cerebras) and dense GPU farms (NVIDIA) to Google Cloud TPUs and other supercompute vendors—and how cloud platform integrations shape deployment, orchestration, and cost. It focuses on hardware architecture, software stack compatibility, networking and memory footprint, and managed vs. on‑premises tradeoffs relevant to training large models and latency‑sensitive inference. As of 2026‑01‑15, organizations must balance raw FLOPS and model parallelism with operational factors: interconnect bandwidth, memory capacity, power and cooling, software ecosystem (framework support, compilers, and profiling), and billing models. Integration tooling matters: MCP (Model Context Protocol) servers let LLM agents operate cloud resources and automate workflows. Examples include an AWS MCP server exposing S3 and DynamoDB operations for data pipelines, a Google Cloud Run MCP server to deploy inference services, an Azure MCP Hub that catalogs MCP servers and deployment patterns, and the GibsonAI MCP server for AI‑powered database build/migrate workflows. These components illustrate how platform integrations reduce friction between model lifecycle tasks (data staging, deployment, autoscaling) and heterogeneous compute backends. Comparing providers requires aligning workload characteristics (training vs inference, batch vs streaming, model size) with provider strengths and integration needs. Key evaluation axes are performance per dollar, integration maturity (APIs, MCP support), developer productivity, and operational risk. This topic aims to help technical decision‑makers understand tradeoffs and plan architectures that pair the right supercompute vendor with cloud platform integrations and LLM‑driven automation.