GPU

A GPU (Graphics Processing Unit) is a type of computer chip designed to handle many tasks at the same time.

Understanding GPUs and their role in large language models has become incredibly important as the DeepSeek vs. OpenAI conversation begins. A lot of terms are being thrown around—reinforcement learning, deep learning, cost efficiency, and the big question: Why is OpenAI so expensive?

But the only thing that truly needs to be understood is GPUs—this is the real key to the difference. Knowing what they do and how they impact training and running large language models (LLMs) is all you need to know.

So, what exactly is a GPU, and why does it matter so much for AI? Let’s break it down.

What is a GPU?

A GPU (Graphics Processing Unit) is a type of computer chip designed to handle many tasks at the same time. Originally made for gaming and visual processing, GPUs are now a key player in AI because they can process massive amounts of data much faster than regular computer processors (CPUs).

Think of a CPU as a smart worker who can handle complex tasks but only a few at a time. A GPU, on the other hand, is like a massive team of workers, each doing simple tasks at lightning speed—perfect for training AI models that require millions of calculations at once.

How GPUs Impact LLMs

LLMs, like ChatGPT and DeepSeek, require a huge amount of processing power to learn from vast amounts of text data. Here’s how GPUs affect AI models:

Training Speed: The more powerful the GPUs, the faster an AI model can be trained. Training a large AI model can take weeks or even months, so high-end GPUs help reduce this time significantly.

Cost Efficiency: GPUs are expensive, and training AI models requires thousands of them. More efficient GPUs mean lower costs, making AI more accessible and affordable.

Performance: When an AI model responds to a question, it uses GPUs to process the request. A stronger GPU setup allows the model to generate responses faster and handle more users at once.

Accuracy & Handling Complex Questions: The more GPUs available, the better an AI model can process complex and nuanced discussions. A model with stronger GPU power can analyze more context, detect subtle meanings, and handle sensitive topics with greater depth. This improves overall accuracy and ensures AI-generated responses are more insightful and relevant.

DeepSeek vs. OpenAI: How They Use GPUs Differently

Recently, DeepSeek and OpenAI have been making headlines, and their approach to GPUs plays a huge role in their differences.

DeepSeek:

DeepSeek has been gaining attention for claiming they trained a large-scale AI model for under $6 million.

How? They optimized GPU usage by using reinforcement learning, which allows the model to improve based on feedback without requiring excessive computing power. This makes their model more cost-effective while still maintaining solid performance.

OpenAI:

OpenAI, on the other hand, relies heavily on deep learning, a more resource-intensive approach that requires massive GPU clusters and significant human involvement to fine-tune models. This allows them to build more powerful and scalable models but also makes the cost significantly higher. This is why OpenAI charges for premium access—maintaining a large, high-performance AI model is expensive.

The Trade-Off: Cost vs. Performance

The big question: Does spending more on GPUs always result in a better AI model? Not necessarily.

DeepSeek’s approach focuses on efficiency—spending less while still delivering strong results.

OpenAI’s approach prioritizes raw power—building models with the most advanced (and costly) hardware.

For users, this means:

  • If you want an AI model that is affordable and efficient, DeepSeek might be appealing.
  • If you want the most advanced, high-performance AI, OpenAI remains the leader.
  • It also depends on what you wish to achieve. Different models excel at different tasks, so aligning AI capabilities with your specific goals is essential.
  • Cultural differences in how the model was developed can shape its behavior, reflecting biases that might not align with user expectations based on their country of origin.
  • Data storage locations can also be a factor. Where your data is stored and processed may impact compliance, security, and privacy regulations.
  • Since DeepSeek is open-source, using it effectively may require additional effort in programming and infrastructure setup, while OpenAI provides a more polished, ready-to-use experience.

My Thoughts

AI is a technology that mimics human cognitive abilities. It’s a technological thinking mind, which means it needs technology, infrastructure, and electricity to function. Think about it like the brain cells in our biological brain—GPUs are the backbone of AI models, impacting speed, performance, and cost.

Think of it like when you’re focusing on something that stretches your mind—you require all your focus, energy, and brain cells. This is the same for AI, but instead of brain cells, we call it GPUs.

It’s really as simple as that. When deciding whether DeepSeek or OpenAI is the right choice for you (or even a different model in the future), the key is to think about the task you need AI to perform and how much “brain power” (technology) it requires.

Using this perspective makes it easier, simpler, and free from hype noise to understand your needs and choose the AI model that best supports your goals.

AI is a thinking revolution. What we believe it to be and what we believe it can do will define how we use it and ultimately what we achieve from it.

Need clarity on what AI is, what it can do, and how it can best support your needs to achieve your goals?

Let’s talk!