As deep learning models evolve, their growing complexity demands high-performance GPUs to ensure efficient inference serving. Many organizations rely on cloud services like AWS, Azure, or GCP for these GPU-powered workloads, but a growing number of businesses are opting to build their own in-house model serving infrastructure. This shift is driven by the need for greater control over costs, data privacy, and system customization. By designing an infrastructure capable of handling multiple GPU SKUs (such as NVIDIA, AMD, and potentially Intel), businesses can achieve a flexible and cost-efficient system that is resilient to supply chain delays and able to leverage diverse hardware capabilities. This article explores the essential components of such infrastructure, focusing on the technical considerations for GPU-agnostic design, container optimization, and workload scheduling.
Why Choose In-House Model Serving Infrastructure?
While cloud GPU instances offer scalability, many organizations prefer in-house infrastructure for several key reasons: