This topic examines the 2026 landscape of AI infrastructure and inference hardware: the vendor silicon (e.g., NVIDIA’s memory‑centric Grace CPUs and inference‑optimized Vera accelerators), collaborations between hyperscalers and model providers (such as Meta), and the software and platform stacks that run models at scale. The confluence of larger foundation models, tighter cost/latency constraints, and rising regulatory and security requirements has shifted investment from raw training throughput to inference efficiency, observability, and governance. Key platform categories intersecting with hardware choices include Decentralized AI Infrastructure (on‑prem, federated, and distributed runtimes that reduce cloud dependency), Edge AI Vision Platforms (on‑device and near‑edge inference for low latency and privacy), AI Data Platforms (pipelines for continuous labeling, retraining and data governance), and AI Security & Governance (audit trails, explainability, and access controls). Representative tools illustrate how software complements hardware: Together AI offers a full‑stack acceleration cloud with serverless inference and fine‑tuning; StackAI provides no‑/low‑code enterprise tooling for building and governing AI agents; Kore.ai focuses on orchestrating regulated multi‑agent workflows with observability; Yellow.ai specializes in agentic CX/EX across channels; and Lindy enables rapid no‑code autonomous agent creation. Selecting an inference stack in 2026 requires balancing latency, throughput, model compatibility, cost, and compliance. Hardware like Grace and Vera can materially reduce memory and latency bottlenecks for large models, while platform choices determine deployment patterns (centralized cloud, edge, or decentralized). Security, governance, and data‑pipeline maturity increasingly drive procurement decisions alongside raw performance.