Google Colab

After tracking Nvidia’s RTX 50-series cards for a while, I still feel they are too expensive and likely not powerful enough to fully support my AI enthusiasm. As a result, I’ve decided to explore alternatives, particularly cloud solutions. Google Colab has turned out to be a very appealing option for now. It provides a Jupyter Notebook interface backed by CPUs, GPUs, and TPUs managed by Google. There is a free tier that offers limited GPU access (availability-based). During my test run this Sunday morning, I was able to access a GPU with 16 GB of memory. Paid users receive access to more powerful GPU and CPU resources. This makes Colab a viable alternative to running an on-premise AI lab—provided you’re comfortable with Google hosting your data and models.

I haven’t checked AWS’s GPU offerings yet, but based on my prior EC2 experience, I feel Colab is much more convenient. Colab simplifies many aspects (from billing to system management), allowing you, as an AI scientist, to focus on model building and code development right away.

One advantage of cloud solutions compared to on-premise setups is that you don’t have to endure noisy fans when GPUs are running, nor do you have to pay for the electricity—something that can be very costly with the latest GPUs.

Of course, there are also disadvantages. For example, connecting your model to on-premise digital assets can be tricky. It’s also unclear how well Colab integrates with tools like Claude Code, although there is built-in Gemini support worth considering.