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NVDA (3/8) ❓ How much is Nvidia betting on Artificial Intelligence? 🟢 Nvidia does not want to just be a player in the AI game. It wants to own the playing field itself. 🟢 One very important insight Nvidia had is that hardware alone is not enough. That is why, alongside chips, it also develops software, developer tools, and AI frameworks. For example, releasing open-source AI models such as Nemotron 3. 🟢 This strategy has two major goals: 1️⃣ Getting developers used to the Nvidia ecosystem 2️⃣ Making software optimized for Nvidia chips from the very beginning This creates a software lock-in effect. Once you enter, it becomes very hard to leave. 🔵 As AI models grow larger and more complex, the demand for more powerful Nvidia chips increases. New architectures like Blackwell are built exactly for this purpose: ▫️ Training very large AI models ▫️ Faster inference and execution ▫️ Better energy efficiency compared to previous generations ▫️ Blackwell chips are essentially complete systems, not just standalone chips 🟢 Another smart move Nvidia makes is acquiring key software companies to strengthen its ecosystem. 🟢 For example, the acquisition of SchedMD, a tool used for managing heavy workloads in data centers, especially for large-scale AI and high-performance computing projects. 🟢 This way, Nvidia is covering the entire stack. As a result: ▫️ It is not dependent on selling a single chip ▫️ It makes it much harder for competitors to enter the market ▫️ It pushes Nvidia to become the industry standard 🟢 Even if a company builds its own custom chip or a new hardware competitor emerges, software, tools, and developers still tend to revolve around Nvidia. #nvda #nvdausdt #nvidia #us_stocks_trading_guide #راهنمای_ترید_سهام_آمریکایی #ethan