Lambda Labs engineer here. Here's what we're trying to argue:
Many benefits of running infrastructure in the cloud are lost for offline batch processing jobs. Training machine learning models doesn't require low response times, high availability, geographic proximity to clients, etc. Yet, with cloud, you're paying for all this extra infrastructure.
The main benefits of cloud for tasks with machine learning type workloads are cost saving (if utilization is low) and no electrical set-up.
On the other hand, cloud is extremely expensive for groups that require high base levels of GPU compute. The article is arguing that such groups can save a huge amount of money by moving infrastructure on-prem.
I feel I am talking to a brick wall. This often happens and this is what exhausts me. You are still arguing between cloud and colo / on-prem (even ignoring that on-prem and colo is very different but w/e) and what I am saying is that there is a third option neither the article nor your reply even acknowledges.
Many benefits of running infrastructure in the cloud are lost for offline batch processing jobs. Training machine learning models doesn't require low response times, high availability, geographic proximity to clients, etc. Yet, with cloud, you're paying for all this extra infrastructure.
The main benefits of cloud for tasks with machine learning type workloads are cost saving (if utilization is low) and no electrical set-up.
On the other hand, cloud is extremely expensive for groups that require high base levels of GPU compute. The article is arguing that such groups can save a huge amount of money by moving infrastructure on-prem.