AI’s GPU obsession blinds us to cheaper and smarter solutions

Opinion: Naman Kabra, co-founder and CEO of Nodeops Network

Graphics Processing Units (GPUs) have become the default hardware for many AI workloads, especially when training large models. That way of thinking can be found everywhere. It makes sense in some contexts, but it also created blind spots that are hindering us.

The GPU has gained a reputation. They are incredible to calculate large numbers in parallel, making them ideal for training large language models and performing fast AI inference. That’s why companies like Openai, Google, and Meta are spending a lot of money building GPU clusters.

While GPUs may prefer to run AI, the Central Processing Unit (CPU) is still very capable. Forgetting this can take time, money and opportunity.

The CPU is not outdated. More people need to realize that it can be used for AI tasks. They sit idle on millions of machines around the world, and can perform a wide range of AI tasks efficiently and affordably, even if they give them the opportunity.

Here, the CPU is shining with AI

It’s easy to see how we came here. GPUs are built for parallelism. They can process huge amounts of data at the same time. This is perfect for tasks like image recognition and chatbot training with billions of parameters. CPUs cannot compete for those jobs.

AI is more than just model training. It’s not just about high-speed matrix mathematics. Today, AI includes tasks like running small models, interpreting data, managing logic chains, making decisions, getting documents, and answering questions. These are not just “silly mathematics” issues. Flexible thinking is required. Logic is required. A CPU is required.

The GPU gets all the headings, but the CPU quietly handles the backbone of many AI workflows, especially when zooming in on how an AI system actually runs.

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The CPU is impressive for what is designed for flexible logic-based operations. They are built to handle one or several tasks at a time. While it may not be impressive next to the massive parallelism of GPUs, many AI tasks do not require such firepower.

Consider autonomous agents, a flashy tool that allows you to use AI to complete tasks like searching the web, writing code, planning projects, and more. Certainly, the agent might call a big language model that runs on the GPU, but everything around it, logic, planning, decisions, is executed successfully on the CPU.

Especially when the model is smaller and optimized, or running in situations where ultra-low latency is not required, you can even perform inference on the CPU (AI-Speak to actually use the model).

The CPU can handle a wide range of AI tasks well. However, since it focuses on GPU performance, it doesn’t use what you already have in front of you.

There’s no need to continue building expensive new data centers packed with GPUs to meet the growing demand for AI. You should use what is already there efficiently.

That’s where things become interesting. Because now we actually have a way to do that.

How a distributed computing network changes the game

A location, or a decentralized physical infrastructure network, is a viable solution. It’s mouthful, but the idea is simple. People contribute unused computing power (such as an idle CPU) that is pooled into a global network that others can take advantage of.

Instead of renting time with a centralized cloud provider GPU cluster, you can run AI workloads on a distributed network of CPUs anywhere in the world. These platforms create peer-to-peer computing layers types that allow jobs to be safely distributed, executed, and verified.

This model has several distinct advantages. First of all, it’s much cheaper. When the CPU works normally, you don’t have to pay a premium price to borrow a rare GPU. Second, it expands naturally.

As more people connect their machines to the network, the available calculations grow. Third, bring your computing closer to the edge. Tasks can be performed on machines near where your data resides, reducing latency and increasing privacy.

Think of it like an Airbnb for calculations. Instead of building more hotels (data centers), we are taking advantage of everything that already has all the empty rooms (idle CPUs).

Shifting our thinking and unlocking scale, efficiency, and resilience by using distributed networks to route AI workloads to the correct processor type, to the GPU if necessary, and to the CPU if possible.

Conclusion

It’s time to stop treating CPUs like second-class citizens in the AI ​​world. Yes, the GPU is important. No one is denying that. The CPU is everywhere. They are not used, but can be enhanced to many AI tasks that we care about.

Instead of throwing more money into the GPU shortage, ask more intelligent questions. Are you using a computing you already have?

With a decentralized computing platform that is stepping up to connect IDLE CPUs to the AI ​​economy, there is a great opportunity to rethink how to expand AI infrastructure. The real constraint is not only GPU availability. It’s a change in thinking. It’s conditioned to chase high-end hardware, which overlooks a potential idle state where the entire network lacks idle state.

Opinion: Naman Kabra, co-founder and CEO of Nodeops Network.

This article is for general informational purposes and is not intended to be considered legal or investment advice, and should not be done. The views, thoughts and opinions expressed here are the authors alone and do not necessarily reflect or express Cointregraph’s views and opinions.