As AI applications become increasingly prevalent in enterprises, more companies are facing challenges with underutilized GPU resources. Both model training and inference processes require powerful GPU resources to support massive computational demands. However, as demand increases, the supply of GPU hardware resources cannot keep up, leading to low resource utilization and rising costs.
To address this challenge, INFINITIX’s AI-Stack platform leverages three core technologies to maximize GPU utilization, enabling businesses to overcome the limitations of inflexible and inefficient GPU usage.
In this article, we will introduce the three key technologies of the AI-Stack platform—GPU partitioning, GPU aggregation, and cross-node computing — and explore how they help enterprises manage GPU resources more flexibly and efficiently to enhance GPU utilization significantly.
The Three Core Technologies of AI-Stack for GPU Compute Resource Management
- GPU Partitioning
- Overview: GPU partitioning technology allows a single GPU to be divided into multiple virtual blocks to meet the training needs of models of different sizes. Through precise partitioning and allocation, a single GPU can handle multiple small tasks simultaneously, significantly improving resource utilization efficiency.
- Efficiency Improvement: With this technology, GPU utilization can exceed 90%, greatly reducing the waste of computational resources. Businesses no longer need to purchase additional GPUs for small tasks, thereby lowering costs.
- Applicable Scenarios: This technology is ideal for businesses with multi-task processing needs, particularly in training small-scale models for multiple tasks. It provides a cost-effective solution for companies with limited GPU resources.
- GPU Aggregation
- Overview: Unlike single GPU partitioning, GPU aggregation technology focuses on combining the computational power of multiple GPUs to meet the demands of training large-scale models. By aggregating the capabilities of multiple GPUs, businesses can tackle more challenging model training tasks with ease.
- Efficiency Improvement: Multi-GPU aggregation significantly accelerates the training speed of large-scale models, enabling the development of extensive AI/ML models more efficiently. This means businesses can launch new products or services in a shorter time frame, enhancing their market competitiveness.
- Applicable Scenarios: This technology is particularly suited for ultra-large models or complex computational applications. For tasks requiring substantial computational power, multi-GPU aggregation can dramatically improve performance, meeting the high-performance needs of enterprises.
- Cross-Node Computing
- Overview: The AI-Stack platform can allocate training tasks across multiple nodes based on demand, leveraging distributed training technology to organize multiple containers into training groups. These groups process massive datasets in parallel, effectively reducing model training time, improving computational efficiency, and maximizing resource utilization.
- Efficiency Improvement: Cross-node computation technology reduces the burden on individual nodes, significantly enhancing the utilization of computing resources. Businesses can use this technology to achieve more efficient workload management, ensuring that every GPU resource is fully utilized.
- Applicable Scenarios: This technology is particularly suitable for applications requiring large-scale computation, such as distributed training in deep learning or high-performance computing (HPC) workloads. Cross-node computation enhances system scalability and flexibility, making it an ideal solution for large-scale AI deployments.
Advantages of AI-Stack
Infinitix’s AI-Stack platform integrates the aforementioned three technologies, providing enterprises with a highly flexible GPU resource management solution. It not only helps enterprises maximize existing hardware resources and reduce costs but also adapts to AI development needs of various scales, ranging from small-scale model training to large-scale distributed computing. Moreover, AI-Stack features a user-friendly interface and comprehensive resource monitoring capabilities, enabling enterprises to easily manage and track GPU resource usage, achieving optimal performance.
Conclusion
As AI technology advances, the demand for GPU computing power will only continue to grow. Given limited hardware resources, improving GPU utilization efficiency has become a pressing issue for enterprises. AI-Stack, through its three core technologies—GPU slicing, aggregation, and cross-node computing—provides enterprises with a highly efficient and comprehensive solution, helping them maintain a competitive edge in the AI race.
