GPU optimization with IBM Turbonomic
Unlock true performance with GPU optimization
As the demand for advanced graphics processing units (GPU) grows to support machine learning, AI, video streaming and 3D visualization, safeguarding performance while maximizing efficiency is critical.
IBM® Turbonomic®, a dynamic IT application resource management software platform, is dedicated to optimizing GPU workloads to promote maximum efficiency without sacrificing performance at the lowest cost.
Turbonomic is committed to developing GPU optimization services to provide performance insights and generate actions to achieve application performance and efficiency targets.
Benefits
Optimizing GPU utilization helps applications to fully leverage their advanced computational power, which then leads to faster response and smoother experiences.
GPUs are resource intensive, including 3D engineering graphics, Gen AI workloads and more. Proper optimization based on demand reduces wasted resources and reduced cost of running graphic-intensive workloads in the cloud.
Properly utilized workloads promote both energy and cost efficiency by cutting resource waste and improving power consumption to reduce carbon impact.
Our commitment to improving GPU optimization
Turbonomic leverages its intelligent analytics to dynamically and continually optimize the utilization of VMs using GPU resources as needed. This will help to ensure the performance of those applications requiring GPU’s and ensuring they are placed on the host with available GPU capacity.
Turbonomic leverages its smart analytical insights to additionally consider GPU metrics in its analysis for GPU-based instances to ensure they are running the optimal GPU-based instance type for best performance and lowest cost.
Generative AI workloads require immense GPU processing power to operate at efficient levels of performance. Turbonomic is working to optimize GPU resources to make sure Gen AI LLM Inference workloads meet defined Service Level Objects (SLO), performance standards, while maximizing GPU usage, efficiency and cost.