The AI Chip Wars: GPU vs TPU vs LPU https://t.co/7XCQkGZkGY
— Abhay 🇸🇬🇮🇳 (@Abhay08)
Jan 25, 2026
Category: Technology Page 1 of 2
Why the future of AI isn’t one chip to rule them all
The 60-Second Primer
Three chips are fighting for AI’s soul. GPUs (Graphics Processing Units) — the Swiss Army knife that trains most AI models today. TPUs (Tensor Processing Units) — Google’s secret weapon, hoarded for its own data centers. And LPUs (Language Processing Units) — the new kid optimized purely for inference speed. Understanding which chip wins where isn’t just hardware trivia — it’s the difference between a startup burning cash on the wrong infrastructure and an enterprise shipping AI that actually responds in real-time.
The Spark That Ignited Everything
I still remember the day my world changed forever. I was fifteen, maybe sixteen, when my father walked through our front door carrying what seemed like a treasure chest – a brand new Windows 3.1 system. The beige tower hummed with promise, and I was absolutely starstruck. While other teenagers were out playing cricket or watching movies, I found myself drawn to this magical box like a moth to a flame.
I’ve had building a RAG on my to-do list for a while. Lately, I’ve been experimenting with Groq, which has consistently impressed me with its inference speed. It’s been over four months since I cancelled my ChatGPT Plus subscription and switched to BoltAI, where I can access leading models like GPT-4, Llama-3.70B, Claude Opus, and Mistral-8x7b at a fraction of the cost. What I appreciate about BoltAI is its well-designed graphical user interface, which allows me to easily connect with multiple foundational models via API.
As a payment enthusiast, I’ve been researching the ins and outs of building a digital wallet that stands out in a crowded market. One thing that’s become clear is that payments themselves are a commodity – everyone wants to offer them, and they’re an essential part of any business transaction. However, the real value lies in what you can build on top of those payments.
In recent times, SaaS solutions built upon Open Source Software (OSS), commonly referred to as Commercial OSS (COSS), have garnered significant attention. This growing trend has made it much easier to develop a comprehensive and scalable web application without resorting to expensive SaaS offerings. Now, all that is required is a team of 3-5 skilled engineers who possess the ability to navigate through extensive code bases. With the aid of tools such as cursor.sh or GitHub Copilot, these engineers can immerse themselves in solving the business or technical challenges at hand, without getting overly fixated on opinionated technology stacks. As someone who tends to get caught up in such stacks, this shift allows me to focus more on the task at hand and avoid unnecessary distractions.
How Cloud Native Applications are Transforming Digital Businesses
Cloud native applications (CN apps) are revolutionizing the way digital businesses operate. Gone are the days of having to rely on bulky, outdated software that takes forever to install and update. Now, businesses can take advantage of the power of cloud native applications to quickly and easily access the latest technology. In a nutshell cloud native applications are born on public or private cloud in a packaged (read containerized) manner that makes their development and launch into the real world environment radically different than traditional .net, php, java applications. This means that they can be deployed quickly and easily, and they can scale up or down as needed. Plus, they are often more secure than traditional applications, since they are hosted in the cloud.
I have been reading about data-driven development productivity and effectiveness for quite some time now and trying to visualise it both from a developer’s shoes, as I have been one for a large part of my career and then from an engineering manager’s eyes where continuous improvement, effectiveness and productivity are a constant topic when it comes to ‘achieving more with less’