💻 Where to Run Models If You Don't Have a Powerful GPU at Home? #MLSTART

Not everyone has an RTX 4090 at home (and that's okay 😄). But that's no reason to postpone experiments with ML and LLMs. At the start, almost everything can be done in the cloud - fast, cheap (sometimes free), and without the pain of setting up the environment.

Here are two options I've used myself 👇

🏟 Kaggle Notebooks

Kaggle is not only about competitions but also a convenient environment for running notebooks with GPUs.

What's good here:
- Free GPU access (usually T4 / P100, with time limits)
- Pre-configured environment: PyTorch, TensorFlow, Hugging Face - everything out of the box
- A huge number of public notebooks - you can see how others solve problems

A great option if you want to learn ML, try fine-tuning lightweight models, or play around with datasets and baseline solutions.

🚀 Google Colab

Google Colab is a classic that many have started (and continue to start) with.

Pros:
- Free GPUs and TPUs (session-based, with limitations)
- Easy to connect Google Drive for data and models
- A huge number of tutorials and examples

If you want more stability and resources, there are paid plans (Colab Pro / Pro+), but the free version is more than enough to start.

💡 What's important to understand: limits are everywhere, sessions may be interrupted, GPUs may be unavailable. This won't be enough for training huge models - but for training small models, experiments, and prototypes, it's not a problem at all.

Most tasks at the start are not about "training a 70B model" but understanding the pipeline and mechanics. Kaggle and Colab cover 80% of educational and experimental tasks and allow you to move forward smoothly.

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