Core Viewpoint - The article discusses the deployment of VLA models and the advancements in AI chip technology, particularly focusing on the capabilities and future plans of the DiGua Robot's AI chips, which cater to consumer robotics and embodied intelligence scenarios [3][4]. Group 1: AI Chip Development - DiGua Robot offers AI chips with computing power ranging from 5 TOPS to 560 TOPS, with the RDK S100 model providing 80 to 120 TOPS and the newly released RDK S600 model offering 560 TOPS, set for product launch next year [4]. - The deployment of models on chips typically requires quantization, converting floating-point models to fixed-point models, which enhances efficiency and reduces power consumption [5]. - The RDK S600 is optimized for deploying large models, achieving throughput rates two to three times higher than mainstream chips for models around 7 billion parameters [4][5]. Group 2: Model Size and Performance - Current models are primarily in the range of 3 billion to 7 billion parameters, which are deemed sufficient given the limited data available for training [6]. - There is a trend towards developing larger models, but the deployment of such models on edge devices may not always be necessary, as smaller models can still perform adequately for specific tasks [7]. - The relationship between model size and reinforcement learning (RL) performance is highlighted, with larger foundational models potentially enhancing RL capabilities [7][8]. Group 3: Lightweight Model Approaches - Recent efforts in lightweight VLA work focus on engineering optimizations rather than merely reducing model size, emphasizing the importance of optimizing operators and compilation strategies [10][11]. - The exploration of model distillation, quantization, and operator-level optimization is crucial for improving deployment efficiency and training speed [12][13]. - The rapid evolution of foundational models in the embodied intelligence space necessitates quick adaptation in deployment and training processes to keep pace with advancements [14].

Group 1 - The core growth engines of the Chinese software industry are identified as AI Agents, multimodal AI models, and model deployment [1] - AI agents are evolving from concepts discussed in enterprise settings to commercially viable products, serving as a new user interface for businesses and knowledge workers [1] - Software vendors are accelerating the integration of AI agents into their professional platforms [1] Group 2 - Analysis of enterprise application project bids from late April to present indicates a robust momentum for projects in Q2 2025 [3] - The release of foundational models like DeepSeek R1 has significantly stimulated demand for privatized AI model deployment among state-owned enterprises, schools, and government clients [3] - AI model deployment projects generally have larger overall scales compared to other ERP or system upgrade projects, often including integrated solutions like computing hardware, which increases contract values [3] Group 3 - Despite solid demand for AI applications on the enterprise side, challenges remain on the consumer side, including low payment conversion rates for consumer-facing AI tools [3] - The revenue contribution from AI functionalities remains small relative to total revenue [3] - AI vendors are continuously pushing multimodal models towards lower costs, higher quality, and longer context, while the acceleration of EDA software localization is seen as a certain growth avenue for AI vendors [3]