don't check nvidia.com/gpu when install watsonx.ai

I have successfully run the granite-code-20b model using only CPU resources in a single-node Ollama environment, with acceptable speed that fully meets the needs of learning and proof-of-concept (POC) testing. This demonstrates that many models in IBM Watsonx.ai do not require GPUs for learning environments/POC scenarios. Therefore, I propose removing the GPU verification step during IBM Watsonx.ai deployment. Doing so would provide customers with a cost-effective path to learn IBM Watsonx.ai. The current version throws a "3 Insufficient nvidia.com/gpu" error when installed without GPUs, whereas IBM Watsonx.ai 4.8.x did not enforce GPU checks. Before committing to generative AI production use, customers always need time to learn and evaluate return on investment (ROI). Purchasing GPUs represents a massive investment, and the rapid iteration of generative AI software/hardware means delaying purchases can yield significant cost savings. However, learning should never be paused.

Of course, IBM Cloud Pak for Data/OpenShift typically operates as a large cluster with multiple machines and abundant CPU resources. IBM should guide customers on leveraging cluster environments to parallelize CPU usage across multiple machines for model execution—something inconvenient to achieve in a single-node Ollama setup. I believe parallel computation across multiple CPU nodes would suffice for basic POC testing. This approach would allow customers to validate concepts cost-effectively while postponing GPU investments until ROI is clearly demonstrated.