Spending money on GPU's was my best investment
Hi, I'm Vikrant, a passionate software developer with a strong belief in the power of teamwork, empathy, and getting things done. With a background in building scalable and efficient backend systems, I've had the privilege of working with a range of technologies that excite me - from Express.js, Flask, and Django to React, PostGres, and MongoDB Atlas. My experience with Azure has given me a solid understanding of cloud infrastructure, and I've had a blast building and deploying applications that make a real impact. But what really gets me going is exploring the frontiers of AI and machine learning. I've had the opportunity to work on some amazing projects, including building advanced RAG applications, fine-tuning models like Phi2 on custom data, and even dabbling in web3 and Ethereum. For me, it's not just about writing code - it's about understanding the people and problems I'm trying to solve. I believe that empathy is the unsung hero of software development, and I strive to bring a human touch to everything I do. Whether it's collaborating with colleagues, communicating with clients, or simply trying to make sense of complex technical concepts, I'm always looking for ways to make technology more accessible and more meaningful. If you're looking for a team player who is passionate about building innovative solutions, let's connect! I'm always up for a chat about the latest tech trends, or just about life in general.
The best investment I made wasn't in stocks, crypto, or day trading.
It was spending money on GPUs to learn how LLMs work
Have you ever tried deploying an model yourself?
Not just used an API.
I mean actually fine tuned a model, set up your own inference pipeline, and deployed an open weight model from scratch.
A lot of people never get to do this, not because they lack curiosity, but because they lack access to powerful GPUs.
I started with what I had: my laptop’s GPU.
And honestly, it was terrible.
Fine tuning a model on it was painfully slow. But that was my starting point.
Later, I moved to Google Colab. I completely pushed its limits too, but I was still able to fine-tune a Phi-2 model. That experience gave me my first real taste of what hands on LLM work actually feels like.
Then I discovered RunPod.
For the first time, I had access to powerful GPUs. I added some money to my account and used those GPUs to train models.
That was one of the biggest leaps in my learning journey.
Suddenly, I could move between epochs at a speed I had never experienced before.
I also deployed an open-weight model on Modal using their initial free credits. That helped me understand how to set up my own inference pipeline instead of just depending on hosted APIs.
Later, one of my friends had to work on a thesis project for their final year of engineering. I helped them with it, and that gave me another opportunity to work hands-on with RunPod GPUs. I created datasets, fine-tuned models, and learned even more by actually building.
Through all of this, I started understanding things that no classroom had taught me properly:
How tokenization works.
Why every model has its own tokenizer.
How model size affects capability.
How LoRA and QLoRA work.
What actually happens when you quantize a model.
Why smaller models can be efficient, but also come with trade-offs.
It was beautiful.
I was learning at a completely different level not through a roadmap, not through a course, and not through formal education.
Just curiosity, experiments, failures, and hands-on building.
I even tried incorporating these learnings into hackathon projects, and the knowledge has compounded over time. It still helps me to this day.
The reason I’m writing this is simple:
Many people wait for the perfect roadmap.
The perfect laptop.
The perfect course.
The perfect time.
But in reality, you start with what you have.
Spend some money on tools that help you learn. That money is not an expense it is an investment in your skills. And in my opinion, it gives far better returns than wasting money chasing quick wins.
I’m still learning and building.
If you relate to this journey, or if you have thoughts, questions, or your own experience with fine-tuning and deploying models, I’d love to hear from you in the comments.
ps: its a image of me hacking on a side project trying to train a U-net model.
