GPT models, with their advanced conversational capabilities, face challenges in local deployment due to high hardware and memory requirements, with larger models needing over 40 GB of GPU memory. A hybrid cloud approach, combining local and cloud resources, offers an efficient solution by balancing workload management, cost, and scalability while adhering to data compliance standards.
Read moreVirtualization Lab with ESXi hypervisor 6.5.x
This is a bit of a dated post, but I’m going to discuss virtualization lab configuration for development and experimentation using ESXi hypervisor 6.5.x. It’s particular relevant now as I’ve setup a Kubernetes cluster at home to develop some orchestration scripts and cluster architecture design for scalable applications. This is a much cheaper solution that deploying a cluster into a cloud-based provider. The only resource cost is power which comes in at a significantly cheaper expense than the per-hour provisioning costs of a cloud provider.
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