How Google Cloud Networking Supports Your Fluid Compute Choices for AI Workloads
The availability of resources for AI workloads can be challenging across the industry, especially accelerators. This can slow your AI workload deployment if it’s built around a specific type of accelerator. The concept of fluid compute allows you to design your AI deployment with several options based on available resources that can fit your use case.
In this blog, we will explore how Google Cloud networking supports your AI workloads and considerations that are relevant to your choice of accelerator (GPU or TPU), as the backend networking component configuration is not exactly the same.
The resource options
After deciding the type of work you want to achieve with your AI deployment, another important component is the actual hardware to get this done. In this case, we want to run inference for a private LLM, and the target is the NVIDIA B200 GPU family which is available in the A4 VMs...
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