Jeff Hinkle, Founder & CEO, Ionstream, says the AI goldrush is great for business and consumers – but demand for enterprise-grade GPUs is outstripping supply.
Big tech is hungry for AI hardware. Its appetite is growing at an extraordinary rate for GPUs which are now the most expensive, and most coveted pieces of technology on the market.
To understand the scale at which AI infrastructure is expanding, you only need to look at Elon Musk’s xAI.
According to a recent press release, xAI has acquired a 1 million-square-foot piece of land in Southwest Memphis to increase its AI data center footprint – in addition to its primary Memphis site and a second new data center in Atlanta.
As part of this remarkable expansion, xAI plans to increase the number of NVIDIA GPUs it owns in 2025 to 1 million – up from 100,000 last year. Meta, OpenAI, and Microsoft (to name a few) are also on hardware spending sprees.
The AI goldrush is great for business and consumers. But there is a problem; demand for enterprise-grade GPUs is outstripping supply. Only last month, OpenAI’s Sam Altman took to X to complain his company is “out of GPUs” which slowed down the rollout of ChatGPT 4.5.
What’s more, smaller tech companies and AI-focused startups are finding themselves at the back of the lunch line, eagerly waiting for access to the latest hardware – or paying above the odds to get it earlier. In a game with first mover advantage, you can appreciate the unfairness of the current landscape.
Choosing the right deployment model – virtualized or bare metal cloud?
With AI models growing exponentially in size, developers need powerful computing solutions that won’t break the bank. In response, traditional cloud options – Cloud GPU and GPU-as-a-service (GPUaaS) – as well as bare metal cloud are fast-emerging services, providing scalable, high-performance computing without delayed access when supply is tight.
Essentially, these services allow users to access and deploy GPUs in the cloud rather than purchasing and maintaining them on-site. Providers have strong relationships with vendors that can open access to cutting-edge hardware for customers of all sizes at fairer market rates. For instance, on-demand access to NVIDIA’s incoming B200 will cost as little as $2.40 per hour via GPUaaS.
There are four main benefits of using cloud GPU or GPUaaS:
- You get scalable performance on demand, overcoming the issue of unpredictable AI workloads. This is a more dynamic solution, aligning computing power with immediate needs, avoiding waste, and delivering cost-efficiency.
- It breaks down the financial barriers in accessing advanced hardware. Purchasing an NVIDIA H200 can cost upwards of $25,000 per unit, but on a pay-as-you-go basis, they can be rented for a low as $2.49 per hour. This model allows companies to focus their capital on testing, improving and growing, instead of being locked into sizeable hardware investments.
- It supports faster time to market. AI is advancing every day and delays can result in competitors gaining ground or getting ahead. By accessing the latest technologies, development cycles are accelerated, and project timelines can be cut down.
- Your maintenance overheads are zero. Installing and running GPUs is just the first step. They need to be maintained and any downtime can be financially crippling. By renting access to GPUs, companies can alleviate the operational burdens and focus entirely on building and scaling their AI models.
Accessing GPUs via bare metal cloud
Bare metal cloud offers the best of both worlds: the raw power of dedicated physical servers with the scalability and automation of cloud computing.
Renting a physical server that provides direct access to GPU hardware instead of relying on virtual machines running on shared hardware can be an alternative option for companies – particularly those prioritizing security, predictability, and customization.
The main features of bare metal cloud are:
- High performance for workloads needing low latency and high compute power (like AI/ML)
- Greater security with a dedicated physical server rather than a shared cloud resource
- Better customization as users can configure the hardware, install specific operating systems and integrate APIs for easy scaling.
The flexibility, cost savings and speed to market make both traditional and bare metal cloud strategic choices for startups looking to grow efficiently and outpace the competition in the fast-moving AI industry.
Choosing the right orchestration tool
LLMs and ML are taking industries to the next level, but with larger, more complex models and datasets, there can be orchestration challenges.
In the case of large-scale GPU workloads deployed in cloud environments, choosing the right orchestration tool is vital for resource and cost efficiency.
To simplify distributed, large-scale training projects, companies can automate the management of computing resources (such as GPUs) across clusters of machines. These can assign workloads to available GPUs, balance computing power across servers, offer scalability based on demand, monitor performance, and detect failures for smoother operations.
There are two main orchestration models – Kubernetes and Slurm – that can handle large-scale GPU projects efficiently and reduce the need for manual management or intervention.
Kubernetes is considered the best option for cloud-based AI and ML workloads and works as a container orchestration platform. If a GPU server crashes, it is self-healing and automatically moves workloads to available GPUs, minimizing disruption and delays.
On the other hand, Slurm (Simple Linux Utility for Resource Management) can manage raw GPU power for companies needing high performance without virtualization overhead – bare metal cloud.
Slurm can help businesses efficiently distribute workloads across thousands of GPUs, schedule jobs to ensure fair resource distribution, save costs and energy efficiency during off-peak hours and ensure reliability in simulations and large-scale experiments such as those found in scientific research and supercomputing.
By choosing the right orchestration tool and deployment model, businesses can optimize performance, cost and scalability for their GPU workloads while controlling costs.
