Embodied Intelligence
Flexible computing power boosts embodied intelligence
With the development of artificial intelligence, embodied intelligence has become a research hotspot. It is attached to physical entities and understands and influences the world through environmental interaction. It is usually manifested as a humanoid robot that integrates multiple subsystems. The progress of general large models and multimodal AI has promoted the application of embodied intelligence, and cross-modal learning has improved intelligence and adaptability. In a specific environment, embodied intelligence relies on small models trained by general large models, which puts higher requirements on data and computing power.
Capabilities
Improve the quality and efficiency of the production and research team
The platform offers comprehensive support for the development and testing of embodied intelligence by production and research teams. It ensures on-demand resource allocation, improves resource utilization, and minimizes issues caused by environmental differences. With development frameworks and tool sets, it reduces deployment pressure on teams. Visual management and streamlined operation and maintenance significantly reduce complexity, enabling teams to focus on business innovation and accelerate model iteration.
Make external services more stable and convenient
A comprehensive and flexible framework supports the business deployment of embodied intelligence. Through a cloud-edge collaborative architecture, resource allocation is dynamically adjusted based on task requirements and real-time load, optimizing cloud and edge resource usage. This ensures balanced system load and enables rapid model deployment and updates.
Enhance scalability and adaptability of AI systems
The platform provides a scalable infrastructure that adapts to the growing demands of AI workloads. By seamlessly integrating with both cloud and edge resources, it ensures that AI systems can scale efficiently without compromising performance. This flexibility allows for easy adjustment to varying task complexities, enabling teams to deploy AI models that can evolve as business needs change.
Challenges
High R&D and operation costs
When developing small models, R&D departments often face the problem of uneven resource allocation. At the same time, the operation and maintenance costs of GPU clusters are high, especially when deployed privately, which requires a lot of hardware investment and maintenance work.
Slow iteration speed
The traditional model development process lacks automation, resulting in a long cycle from development to testing to deployment and slow iteration.
Business deployment is complex and performance optimization is difficult
In different business scenarios, it is a challenge to optimize model performance to meet the requirements of real-time, accuracy and resource efficiency. The deployment of large cloud models and edge inference clusters involves multiple components and environments, and the deployment process is complex and difficult to manage.