This topic examines the landscape of commercial and open‑source AI models used for climate and weather simulation, centered on NVIDIA’s Earth‑2 suite and its competitors, and the infrastructure that makes high‑resolution forecasting practical. It covers model families (proprietary multi‑physics and large multimodal emulator models vs. community open models), the data platforms that feed them, and the compute and deployment stacks needed to run at scale. Relevance in early 2026 stems from growing demand for finer spatial/temporal forecasts, tighter integration of satellite and sensor data, rising energy and compute costs, and a stronger push for reproducibility and governance in climate modeling. These pressures favor both specialized commercial offerings and modular open ecosystems that can be audited and adapted by research groups. Key tools and roles highlighted here include: Rebellions.ai — purpose‑built, energy‑efficient inference accelerators and software for hyperscale inference; Tensorplex Labs — decentralized, open infrastructure for collaborative model development and novel governance/finance primitives; Stable Code and CodeGeeX — code‑focused LLMs that accelerate model development, parameter tuning, and pipeline scripting; Replit — web‑native IDE and hosting for prototyping simulation code and lightweight services; and MindStudio — no‑code/low‑code environment for assembling, testing, and operating agentized workflows and deployment pipelines. Taken together, these capabilities spotlight two converging trends: hardware/software co‑design for cost‑effective, large‑scale inference, and an expanding ecosystem of developer and governance tools that make advanced climate AI more accessible and operationally robust. The practical comparison emphasizes interoperability, energy efficiency, reproducibility, and the balance between commercial support and open scientific scrutiny.