Lower GPU utilization

The GPU memory is not being used to its full potential, with average utilization remaining at 30-50​ ​%, resulting in a mismatch between the GPU task size and the GPU memory size, resulting in wasted resources.

Not allocating the appropriate GPU for the workload

It is not possible to manage GPU resource usage in real time, making it difficult to optimize allocation or make rational investment decisions when expanding the GPU environment.

Knowledge acquisition of Kubernetes

One possible solution to the above problem is to use Kubernetes, but the high hurdles for learning and operation are a problem for AI engineers.

Supports GPU splitting, merging, and cross-node calculations

AI-Stack supports NVIDIA​ ​GPUs and can flexibly perform splitting, merging, and cross-node calculations. By finely allocating resources according to task needs, it achieves efficient operation.

For small projects, GPU partitioning allows for efficient use of computing resources, while for high-load processing such as training large-scale language models, GPU integration and multi-node calculations maximize performance.

Build an AI development environment in just one minute

Previously, it was not uncommon for AI researchers to spend one to two weeks applying for a development environment and installing packages. With AI-Stack, you can build a container environment in less than a minute with just a few clicks on the intuitive UI and start model training immediately, dramatically improving development efficiency.

Rapid Container Service (RCS): Enabling instant deployment of AI services

RCS is a Kubernetes-based rapid deployment solution designed for AI inference and application services. It supports flexible configuration of service specifications, container management, automatic scaling, and rolling updates, significantly improving the efficiency and stability of AI operations.

Key features of RCS:
■Fast deployment ■Real-time monitoring ■High scalability ■Efficient version control

Monitoring visualization dashboard

AI-Stack comes with an integrated dashboard that allows administrators and IT teams to centrally monitor the load status of each GPU, memory usage, and computing resource usage by project.
Graphical data displays visualize resource usage and operational costs, supporting optimal resource allocation and future investment decisions. Real-time alerts also ensure stable operation of AI systems.