G2

Rishabh S.

Software Engineer

Mid-Market (51-1000 emp.)

8/7/2026

"Fast GPU Training with Deep Learning VM"

4/5

What do you like best about Google Cloud Deep Learning VM Image?

Honestly, what I like most is that it saves me from all the boring, frustrating setup work. I used to lose half a day wrestling with GPU drivers and double-checking that my CUDA and cuDNN versions actually lined up with TensorFlow or PyTorch, and it was always a headache. With the Deep Learning VM, most of that is essentially handled for you. I can spin up an instance, choose the GPU I need, and be training models within minutes instead of wasting an entire afternoon on configuration.

It also integrates smoothly with the rest of Google Cloud, so pulling data from Cloud Storage or BigQuery doesn’t turn into another chore. And since Jupyter is ready from the start, I can jump straight in and begin experimenting right away. Review collected by and hosted on G2.com.

What do you dislike about Google Cloud Deep Learning VM Image?

The cost is a big downside—GPU instances add up fast, especially if you forget to shut one down after a training run (which I’ve definitely done more than once, and regretted it when the bill showed up). The pre-installed package versions can also be a bit of a headache if your project needs something slightly different; you still end up doing manual tweaks, which kind of defeats the purpose. And honestly, the sheer number of image and machine-type options can feel overwhelming at first. It took me a while to sort through them and figure out which combination actually made sense for what I was trying to do. Review collected by and hosted on G2.com.

What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?

The biggest problem it solves for me is environment setup and consistency. Before, every time I wanted to train a model, I had to double-check that my GPU drivers, CUDA version, and framework versions all lined up correctly—and that alone could easily take a whole day, especially if something broke halfway through. With the Deep Learning VM, most of that is handled for me, so I can go from “I need to run this” to actually training a model in minutes instead of hours.

It’s also been helpful for scaling. When I need more compute power for a bigger job, I can spin up a larger instance rather than being limited by whatever hardware I have locally. And because it ties into the rest of Google Cloud, moving data around or working with storage and other services doesn’t require a bunch of extra setup.

Overall, it’s saved me a lot of time and frustration that would otherwise go into infrastructure babysitting. That means I can spend more of my time actually working on the model—or on the problem I’m trying to solve—instead of troubleshooting the environment. Review collected by and hosted on G2.com.