To The Moon

$NVIDIA(NVDA)$

In the increasingly competitive global semiconductor industry, as one of the world's most valuable semiconductor companies, Nvidia is actively engaged in the field of robotics technology, striving to break through the competitive bottleneck of its core AI chip business and explore new growth paths.

Layout characteristics

1. Launching a dedicated computing platform: Nvidia plans to launch the Jetson Thor next-generation compact computer for AI training of humanoid robots in the first half of 2025. This computer adopts a modular architecture and has been optimized in terms of performance, power consumption, and size. It is equipped with Nvidia Blackwell architecture GPU and has 8-bit floating-point AI performance of up to 800 teraflops, providing strong support for intelligent computing of robots.

2. Provide full stack solutions: NVIDIA's layout not only covers hardware devices, but also includes software for training AI robots and high-performance computing chips, forming a complete combination. Its Omniverse platform supports 3D collaboration and digital simulation, helping developers efficiently train and test robots in virtual environments, greatly simplifying the development process of robots.

3. Strategic investment and cooperation: NVIDIA has rapidly expanded its ecological landscape in the field of robotics through active investment and cooperation strategies. Jointly investing in humanoid robot company Figure AI with Microsoft, OpenAI, and other companies, and also partnering with companies such as Apptronik to inject strong momentum into the development of humanoid robots, accelerating the application and landing of technology.

Distinctive features

1. Powerful AI performance: Jetson Thor has excellent AI performance, capable of carrying complex AI models and multimodal generative AI models, endowing robots with more natural and smooth interaction capabilities, enabling them to perform outstandingly in complex task processing and bringing users a new experience.

2. Leading simulation technology: NVIDIA's simulation technology has achieved significant results in solving the gap between virtual and real applications of robots, effectively addressing the Sim to Real gap problem. This ensures that well-trained robots in virtual environments can seamlessly connect to real scenes, perform tasks accurately, and improve the practicality and adaptability of robots.

3. Wide industry applications: Nvidia's technology has emerged in multiple industries, such as Amazon deploying its robot simulation technology in warehouses, and companies like Toyota and Boston Dynamics adopting its training software, fully demonstrating the reliability and wide applicability of its technology and providing key support for intelligent upgrades in different industries.

Challenges Faced

1. Market education and acceptance: Although robot technology has broad prospects, the current market's understanding and acceptance of robots are still insufficient, and their application scope is relatively limited. Nvidia needs to invest more effort in market education and promotion to enhance market recognition of the value of robots and expand market space.

2. Technological maturity and reliability: Robot technology still has significant room for improvement in key indicators such as safety, reliability, and accuracy. This is crucial for meeting the strict requirements of diverse application scenarios