*This is a summary of the white paper “Top Considerations When Building Out Your AI/ML Infrastructure.” Download your copy here.
AI is transitioning from innovative technology to mainstream. In fact, IDC expects that 90% of new enterprise applications will use AI by 2025. For organizations to stay competitive, they must consider how to build their AI/ML infrastructure.
There is a misconception that simply adding GPUs is enough to tackle increasingly complex AI problems. However, this approach leaves organizations with inefficient hardware that uses excess energy, is slow to perform AI tasks, and needs more frequent upgrades.
Companies must choose hardware components carefully if they want infrastructure that performs optimally. Let’s take a brief look at which elements make the biggest difference for AI computing loads.
7 Factors That Impact AI Performance
High-performing AI applications require a mix of carefully selected components, fine system control, and highly visible administrative tools. The following are hardware and configuration factors that make the difference between an efficient setup and one that bleeds resources.
1. CPUs. AI workloads perform a significant amount of parallel processing. This type of processing is usually best offloaded to GPUs, which have thousands of cores adept at handling repetitive tasks. However, depending on the complexity of the tasks, specialized microprocessors like the Intel Xeon line and FPGAs may be the right choice.
2. Memory. Machine learning models are memory intensive and require fast, direct memory. For example, GPU memory and GPUDirect RDMA bypass the CPU and main system memory for improved performance. AI and ML workloads require more memory than conventional computing, and workloads are often spread over several GPUs for best performance.
3. Storage. AI data sets are enormous, and complex models can take weeks to run. Processing these vast amounts of data means that AI infrastructure needs fast storage so that systems aren’t running into bottlenecks, which can further slow down data processing.
4. Bandwidth. Large data sets present challenges for those relying on the cloud. One complication is the high cost of retrieving data, and another is speed. Data sets that are a petabyte or more can take days to upload to the cloud — so businesses should weigh the pros and cons of local processing.
5. Workload Characteristics. Different types of AI workloads will change your computing requirements. For example, some workloads require multiple tests, which can increase costs considerably when teams need to constantly retrieve data from the cloud.
6. Software. Development toolkits are becoming more specialized due to increasingly complex AI applications, like self-driving cars. To ensure this software functions properly and doesn’t waste resources, organizations must customize their hardware to match their use case.
7. Administration. The complexity of modern AI applications makes it more important for businesses to have in-depth monitoring tools. These tools should allow teams to monitor KPIs and control hardware in both the cloud and on local machines.
Get the Best of Infrastructure Flexibility and Cost Control
AI workloads need flexible infrastructure. Companies can make themselves more agile by combining cloud and local resources. They can then take advantage of specialized hardware that helps process heavy AI and ML workloads quickly and cost-efficiently. At the same time, this combination will provide the flexibility of the cloud to scale infrastructure and access pivotal tools from anywhere.
It’s clear that modern AI infrastructure requires hardware customized to your use case. Intequus can help you build hardware that will give you the right blend of efficiency, flexibility, and power. Talk to our team if you want to secure your organization’s future with custom AI infrastructure.