AI Inference Use Cases
For LLMs, using CPUs can be more efficient for data preprocessing, running non-parallelizable parts of algorithms, and orchestrating the training process.
Whether you are using OpenCV or other alternatives, CPUs provide a robust foundation for computer vision use cases in industries such as automotive, healthcare, and more.
Providing your customers a customized experience is easier than you think by leveraging CPUs to make accurate recommendations.
CPUs can form the backbone of any robust generative AI application. CPUs can ensure that the entire AI server system runs according to plan by taking on tasks that do not require intense parallel processing including managing the software stack, handing input/output operations, and the orchestration of operations.
CPUs can manage sensor data, make real-time decisions, and ensure out of bounds exceptions do not occur.
These tasks typically involve analyzing patterns in data to detect deviations or anomalies, which can be effectively processed using CPU cores.
CPUs can take on many AI processing tasks, avoiding the use of costly GPUs and associated power costs.
CPU-based bare metal servers offer superior performance and reliability compared to virtualized environments, thanks to their dedicated hardware resources and absence of hypervisor overhead. Our global network offers low-latency connections to the outside world.
Many AI frameworks and libraries, such as TensorFlow, PyTorch, and scikit-learn, are optimized to run efficiently on CPUs. This compatibility ensures that a wide range of AI software can be readily deployed on CPU-based systems without significant modifications.
Additional CPU-based bare metal servers can easily be added which provides flexibility for changing AI workload requirements, whether it’s handling increased data volume or accommodating more complex models.
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