What is a GPU cloud and why are all startups paying millions for it

What is a GPU cloud

A cloud is when, instead of buying your own servers, you simply rent them over the internet. Need computing power? Turn it on. Task completed? Turn it off. You pay only for those hours. This is exactly how the largest platforms—AWS, Google Cloud, and Azure—operate.

Traditional clouds were built for standard tasks: websites, databases, and business applications. When AI emerged, it became clear that conventional servers were insufficient. Neural networks require a specific type of computation that standard processors simply cannot handle.

A GPU cloud provides access to graphics processing units over the internet without the need to purchase physical hardware. A single modern GPU, such as the NVIDIA H100, costs between $25,000 and $40,000 per unit. A GPU cloud removes this barrier: you get the necessary computing power when you need it and avoid spending money on idle time.

How a GPU differs from a standard processor

A standard processor, or CPU, is designed for heterogeneous tasks. It solves them sequentially—one after another.

A GPU is structured differently. Inside, it has anywhere from a thousand to nearly 17,000 cores that work in parallel, simultaneously performing billions of simple, identical operations.

Memory bandwidth for GPUs reaches up to 3,350 gigabytes per second, whereas for CPUs it is around 50.

This architecture is essential for training neural networks. There is no complex logic involved—instead, there is a massive number of identical mathematical operations that must be performed simultaneously, not sequentially. CPUs cannot handle this not because they are inferior, but because they are designed for different purposes.

Why is this necessary

It is impossible to train a modern language model or run computer vision without GPUs. This is not a matter of convenience—it is a matter of the physics of the task.

Training a large language model requires tens of thousands of GPUs running continuously for weeks. For inference—that is, the model answering user queries in real time—GPU clusters are needed to process millions of requests per day. Without this hardware, the product simply does not exist.

Why startups pay millions for this

GPU computing is the largest expense for any AI startup. Without GPUs, you cannot train a model, launch a product, or process user requests. This is not an optional feature—it is a mandatory requirement for operation.

At the same time, GPUs remain a scarce commodity. Demand for them is growing faster than they can be produced. Prices remain high, and startups have no leverage to influence this—they must pay according to market conditions.

This is precisely why GPU clouds consume such a large portion of the budget: not because companies are overspending, but because their product would not exist without this computing power.

Where to get GPUs: two paths

In the past, startups had little choice but to use AWS, Google Cloud, or Azure. These three major cloud giants controlled the market. However, they were designed for large businesses with substantial contracts and lengthy approval processes.

Today, specialized platforms have emerged alongside them—CoreWeave, Lambda Labs, RunPod, and GMI Cloud. They focus exclusively on GPUs and offer the same hardware, but with greater simplicity, speed, and lower cost.

So why do companies still go to AWS or Google Cloud? Because they offer more than just GPUs. Managed databases, analytics tools, global infrastructure, and security compliance—all in one place. If a startup needs all of this, the premium is justified. If not, specialized platforms provide the same hardware at a fraction of the cost.