Opinion by: Kabra, co-founder and CEO of Nodeops Network
Graphic processing units (GPU) have become a default hardware for many AI workloads, especially when training large models. That thought is everywhere. While it makes sense in some contexts, it also creates a blind spot that prevents us.
GPUs have achieved their reputation. They are incredible in crunching massive numbers in parallel, making them perfect for the practice of large language models or running high-speed AI recognition. That’s why companies like OpenaiGoogle, and Meta spend a lot of money on building GPU clusters.
While GPUs may prefer for AI operation, we will never forget about central processing units (CPU), which is still capable. This forgetting can cost us time, money, and opportunity.
CPUs are not outdated. Many people need to realize that they can be used for AI activities. They sit without doing millions of machines around the world, with the ability to run a wide range of AI activities that are great and affordable, if we just give them a chance.
Where cpus shine in AI
It’s easy to see how we got here. GPUs were built for parallelism. They can handle enormous amounts of data at the same time, which is great for tasks such as recognizing the image or training a chatbot with billions -billions of parameters. CPUs cannot compete with those jobs.
AI is not just model training. Not only is this high-speed matrix math. Today, AI includes tasks such as running smaller models, interpreting data, managing chains, making decisions, obtaining documents, and answering questions. These are not just “dumb math” problems. They require flexible thinking. They need logic. They require CPUs.
As GPUs get all the headlines, CPUs quietly holds the spine of many AI flows, especially if you are hovering in how AI systems are really running in the real world.
Recently: ‘Our GPUs are melting’ — Openai will place the limiter after Ghibli-Tsunami
CPUs are impressive in what is designed for: flexible, logic-based operations. They were built to hold one or several activities at the same time. That may not be amazing next to the massive similarities of GPUs, but many AI activities do not require that kind of firepower.
Consider autonomous agents, fancy tools that can use AI to complete tasks such as web search, writing code, or planning a project. Sure, the agent can call a large language model running on a GPU, but all around it, logic, planning, decision making, just run into a CPU.
Even the foresight (AI-Speak for actual use of the model after training) can be done with CPUsEspecially if the models are smaller, optimized, or running in situations where ultra-low latency is not required.
CPUs can handle a large range of AI tasks to properly. We are focused on GPU performance, however, that we do not use what we already have in front of us.
We do not need to keep developing expensive new data centers full of GPUs to meet the growing demand for AI. We just have to use what’s out there very well.
That’s where things get interesting. Because now we have a way to actually do That is
How Decentralized Compute Networks Change Game
Dependsor decentralized physical infrastructure networks, is a viable solution. It’s a mouth, but the idea is simple: people contribute to their unused computing power (such as idle CPUs), which will get a global network that can tap others.
Instead of renting time to some cloud provider’s GPU clusters, you can run AI workloads on a decentralized CPU network anywhere in the world. These platforms create a type of peer-to-peer computing layer where jobs can be distributed, executed, and proven safely.
This model has some clear benefits. First, it’s cheaper. You do not have to pay premium prices to rent a difficult GPU when a CPU will only do a job. Second, this is a natural scales.
The available compute grows as more people plug their network machines. Third, it brings computing near the edge. Tasks can be operated on machines near where the data lives, reducing latency and increasing privacy.
Think of it like Airbnb for compute. Instead of building more hotels (data center), we better use all the empty rooms (idle CPU) already already.
By transferring our thinking and using decentralized networks to cover AI workloads in the right type of processor, GPU if necessary and CPU if possible, we will unlock scale, efficiency, and elastic.
The bottom line
It is time to stop treatment with CPUs such as citizens of the second type in the AI world. Yes, GPUs are critical. There is no denial of it. CPUs are everywhere. That they -underused but perfectly capable of powerful to many of the AI activities we care about.
Instead of throwing more money on GPU lack, let’s ask a smarter question: do we use the computing we already have?
With the decentralized compute platforms that climb up to connect the CPU idle to the AI economy, we have a huge opportunity to re -imagine how we can measure AI infrastructure. True compulsion is not just having GPU. This is a mindset shift. We are conditioned to chase the high-end hardware that we do not notice the unfinished potential seating throughout the network.
Opinion by: Kabra, co-founder and CEO of the Nodeops Network.
This article is for general information purposes and is not intended to be and should not be done as legal or investment advice. The views, attitudes, and opinions expressed here are unique and do not necessarily reflect or represent the views and opinions of the cointelegraph.
