The rapid growth of Generative AI is driving innovation, transforming industries — and significantly increasing the demand for compute. Many AI start-ups are falling into what’s called the “compute trap” and prioritizing access to cutting-edge hardware despite the cost, rather than optimizing their existing infrastructure or finding more effective and efficient methods for building and deploying GenAI applications.
While GPU power will always be essential for training large-scale AI models and other machine learning tasks, they are just one piece of the puzzle. Without similarly state-of-the art CPUs, high speed network interface cards like the InfiniBand 400 ND, and DDR5 memory, as well as the motherboard and server rack necessary to deploy them, it’s just not possible to get the headline performance from an NVIDIA H100 and other top-spec GPUs. Adopting a broader perspective on compute, along with a holistic approach to AI development that includes optimized data preparation, training efficiency, and scalable inference infrastructure, can lead to more sustainable growth for AI applications.
The Compute Dilemma: More Isn’t Always Better
All else being equal, more compute and a larger dataset means more powerful AI models. Meta’s Llama 3.1 8B and 405B LLMs, for example, were trained on the same 15 trillion token dataset using NVIDIA H100s – but the 8B version took just 1.46 million GPU hours to train while the significantly more powerful 405B version took 30.84 million GPU hours – over 21 -times longer.
And in the real world, all else is seldom equal. Not many AI companies have the resources to compete with tech giants like Meta. Rather than falling into the compute trap and over-investing in hardware access, many companies could see greater success by focusing on optimizing their entire technology stack.
Even though Llama 8B isn’t as powerful as Llama 405B, it still outperforms many older, larger models. While Meta used a lot of compute in developing Llama, its success also stems from innovations that extend beyond the latest GPUs.
Streamlined AI Growth With a Unified Tech Stack
Managing the full lifecycle of ML development on a single platform – from data preparation and labelling to model training, fine-tuning and even inference – can bring many advantages.
Using a single full-stack provider means teams only have to learn a single set of tools, simplifying things across your organization. Additionally, keeping all data on one platform avoids the inefficiencies of multi-cloud environments. Another major advantage is that, in case of issues, support is streamlined as your provider understands your entire stack.
There are potentially financial benefits as well. Consolidating data processing, training and inference on one platform can result in more favorable pricing, leading to lower AI development costs overall.
Beyond Big Cloud: Finding the Right Fit for AI Development
While hyperscalers like AWS, Microsoft Azure and Google Cloud are popular choices for many applications, they can have downsides for AI and ML companies.
The Big Three cloud platforms are expensive, and unless you run a massive company or have substantial venture funding, their services may not offer the best return on investment. Moreover, these platforms aren’t optimized for AI and ML specific tasks, so you can pay a significant premium for features you don’t need.
Platforms such as Nebius, for example, which are designed specifically for AI development, offer a more affordable and efficient alternative. These AI-focused full-stack providers offer compute on hardware tailored to AI tasks, ensuring that your models are running optimally without unnecessary infrastructure complexities. Whether you’re training or deploying a model for inference, you can be confident that you are using the right tools for the job without having to navigate a sprawling server backend. Best of all, no more wondering why your expensive GPUs aren’t performing as they should.
While building an AI application with a holistic approach requires upfront planning, it can significantly reduce long-term infrastructure costs. Not only will optimized applications cost less to train, but inference costs will be lower too. These efficiencies can compound over multiple generations of AI models and be the edge that gets your company to an IPO.
