Artificial Intelligence & Machine Learning

It is now widely accepted that companies that fail to incorporate the potential of AI into their business strategies will find themselves behind the competition. Yet, many companies are still ill-equipped to implement a comprehensive AI strategy. A pivotal consideration to successfully deploying AI is to build the proper infrastructure foundation to support the constant demands of AI/ML applications. Traditional infrastructure architectures designed to power established, conventional workloads cannot be effectively used to power AI/ML workloads that demand a high-performance, elastic and cost-efficient infrastructure.

AI/ML applications are typically characterized by the need to process vast datasets that also fluctuate unpredictably in volume. To cope with the intensive processing requirements, compute elements (e.g., CPUs and GPUs) cannot afford to work at suboptimal efficiency and utilization. CPUs must be fully allocated to running the applications instead of wasting cycles inefficiently executing infrastructure computations. GPUs with an enormous appetite for ingesting data – must not be trapped behind CPU bottlenecks, resulting in idle cycles and low utilization. And to process and store large volumes of data, storage must likewise be efficient and highly utilized. Direct-attached storage (DAS) may work well for traditional workloads but is handicapped for massively scaled-out applications like AI/ML. In some AI/ML applications, e.g., inference at the edge, low latency is critical to provide real-time results. Thus, all infrastructure resources, including the network, must work together to meet this stringent performance requirement while ensuring that footprint and power constraints are met.

Fungible offers a high-performance, highly cost-effective, natively scale-out data center infrastructure solution that allows your organization to consolidate your AI infrastructure into lower-cost, footprint solutions and to respond quickly (literally in minutes!) to changing workload requirements. When a workload is completed, you can rapidly reallocate resources to a different workload.

Don’t just aim to match the pace of progress. Prepare to beat it.