AI requires infrastructure, but it's more than just infrastructure. Training and running AI models often relies on special infrastructure resources, such as GPU-equipped servers, which enable parallel computation of AI workloads that require large amounts of computing resources. .
Unfortunately, sourcing the right infrastructure for AI is not always easy. With the continuing GPU shortage, getting the right hardware can be difficult, not to mention costly. While it is possible to use cloud-based infrastructure for AI workloads, that approach comes with its own set of challenges.
Enterprises have several options for building or accessing the infrastructure needed for their AI workloads. First, organizations should determine their key infrastructure requirements for AI and evaluate the pros and cons of different approaches to acquiring that infrastructure.
AI infrastructure requirements
Your exact infrastructure needs will depend on the specifics of each AI workload. However, organizations looking to deploy their own AI models or applications typically need:
- computing resources, which is important not only for analysis but also for training AI models. While you may be able to use a standard CPU for machine learning workloads, GPU-enabled hardware is often a better choice for use cases that require extreme computing power.
- rumprovides short-term storage during model training and data processing.
- Persistent storage resourcessave the training data.
Four strategies for procuring AI infrastructure
How can organizations obtain the infrastructure that AI requires? Compare these viable approaches.
1. Buy new hardware
The easiest option is to purchase new servers optimized for AI workloads. This approach allows businesses to obtain exactly the right hardware for their use case.
The obvious drawback is that purchasing AI infrastructure outright can be very expensive. The cost of a single GPU-enabled server can be tens of thousands of dollars, and large-scale AI deployments can require dozens or even hundreds of such servers.
Organizations that choose this option will also need to set up and maintain servers in-house, increasing the operational burden on their IT teams. Therefore, for organizations that don't have the budget or budget to dedicate human resources to managing new AI servers, the approach of “buying” AI infrastructure may not be the right choice.
2. Reuse existing servers
Instead of purchasing new servers, you can also reuse existing servers for your AI workloads. This is an especially good option for businesses that have spare servers available. For example, a company that recently moved its workloads to the public cloud and no longer needs all of its on-premises servers.
The challenge here is that not all servers can support the unique infrastructure needs of AI workloads. For example, not all servers include an interface that allows IT staff to install GPUs.
Organizations looking to reuse existing servers will also need to purchase additional hardware components (such as GPUs) required for AI workloads. In other words, this strategy is not revenue neutral. But overall, it's still more cost-effective than purchasing entirely new AI infrastructure.
3. Use GPU as a service
Organizations whose primary AI infrastructure requirement is GPUs should consider offering GPUs as a service.
This option gives your business access to GPU-enabled servers hosted in the cloud. This is the same idea as renting virtual servers on traditional public cloud IaaS platforms. However, with GPU as a Service, the server you rent includes a GPU.
This is a good approach for organizations that only need temporary access to GPUs, such as for training AI models. It also gives you the flexibility to choose between different types of his GPU-enabled server configurations.
The main challenge with GPU as a service is the need to migrate AI workloads to GPU-enabled infrastructure, which requires some effort. Organizations that use rented servers to process user data may also run into data governance and privacy issues, as potentially sensitive information is exposed to third-party platforms.
4. Use GPU over IP
Another way to get GPU-enabled infrastructure for AI without setting it up in-house is to use GPU-over-IP services.
This approach uses networking to connect an AI workload hosted on one server to GPU resources in another server, even if the server hosting the workload does not have a GPU. As a result, organizations no longer need to migrate workloads or expose data directly to third-party infrastructure. Everything remains on the company-controlled servers, except for parallel computation, which is enabled by GPUs on remote servers.
GPU over IP is a relatively new idea, and to take advantage of it you need to find a GPU-enabled server with plenty of capacity. However, it offers several convenience and data privacy advantages over traditional GPU-as-a-service approaches.
Questions to ask when choosing an AI infrastructure
When choosing the options above, consider the following questions to choose the right AI infrastructure approach:
- How much is the budget? The more money available to invest in AI infrastructure, the more likely it is to acquire and manage servers for AI workloads.
- How long will I need the infrastructure? For one-time model training, it makes more sense to choose GPU as a service or GPU on IP. However, organizations with ongoing needs for AI infrastructure may find it worthwhile to build their own infrastructure.
- How sensitive is my data? Organizations that cannot risk exposing their data to third parties should build an in-house AI infrastructure or consider GPU-over-IP options.
- What are your human resources? Companies planning to build their own AI infrastructure need staff skilled in AI infrastructure management. Using GPU over IP may also require specialized skills. By comparison, his GPU as a service requires minimal management and maintenance effort, as an engineer only needs to spin up his AI infrastructure and his resources in the cloud.
