This topic examines how NVIDIA‑led AI‑native network stacks stack up against incumbent telco vendor solutions as operators plan for AI‑intensive 5G evolution and early 6G architectures. The core question is how to combine low‑latency, GPU‑accelerated model inference and distributed training with telecom domain integration (RAN, core, OSS/BSS), regulatory constraints, and edge deployment patterns. NVIDIA and hyperscaler‑style stacks prioritize hardware acceleration, scalable model orchestration and unified software toolchains that enable high‑throughput inference and model lifecycle management at the edge and cloud. In contrast, telco vendor solutions emphasize integrated operations, standards compliance, network functions integration and lifecycle support tailored to carrier workflows. Both approaches intersect with three critical categories: AI Automation Platforms (real‑time agent and workflow automation), AI Data Platforms (feature/annotation, retrieval, and private LLM hosting) and Decentralized AI Infrastructure (edge clusters, federated learning and on‑premises inference). Key vendor and platform examples illustrate the landscape: Vertex AI and Google Gemini address model training, multimodal inference and managed deployment; Cohere offers enterprise LLMs, embeddings and search for private deployments; Together AI and similar acceleration clouds focus on high‑performance training and low‑latency inference; Observe.AI and Yellow.ai provide agentic CX/CC automation and real‑time assistance; Anakin.ai enables no‑code automation for rapid prototyping. For telcos, the tradeoffs are clear: NVIDIA‑style stacks deliver raw AI performance and rapid innovation, while telco vendor solutions provide tighter network integration, operational maturity and regulatory alignment. Choosing a path—or combining both—depends on latency, data sovereignty, lifecycle operations and the extent of edge decentralization required for 6G use cases.