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We define Pipeline minutes as the execution time for your pipelines. You bring up an interesting point, though. So today, for our Linux Runners on GitLab.com, those Runners are currently offered only on one machine type, Google Compute n1-standard-1 instances with 3.75GB of RAM. Our current Windows Runners on GitLab.com are Google Compute n1-standard-2 instances with 2 vCPUs and 7.5GB RAM.

In the future, for Linux and Windows Runners, we will offer more GCP machine types. For our soon to launch macOS Build Cloud beta, we are planning to start with one virtual machine size and then possibly offer different machine configurations at GA.

And yes - the virtual machine used for your build on GitLab.com are dedicated only to your pipeline job and immediately deleted on job completion.

Finally, the only way to know how long your current build job will take on a GCP n1-standard-1 compared to the 8-core machine is to run the job and compare the results. I assume that your 8-core machine is probably a physical box, so you should of course, get better performance than a 1-2 vCPU VM.

A few reference links:

https://docs.gitlab.com/ee/user/gitlab_com/#shared-runners

https://gitlab.com/groups/gitlab-org/-/epics/1830

https://gitlab.com/groups/gitlab-org/-/epics/2426

Darren Eastman: Product Manager GitLab Runner




> In the future, for Linux and Windows Runners, we will offer more GCP machine types.

I can see free plan users starting to see their CI jobs randomly timeout if they have access to those machines.

I get that execution time isn't a bad metric, all things considered. But I would have expected actual CPU `time` (1), maybe mixed with memory usage.


AWS or GCS charge for VMs in actual clock (wall) time though, right?

Tthat your load may spend more or less time waiting on IO instead of actually using the CPU... I would not expect to effect your charge. Which is the main difference between wall time and actual CPU time, right?




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