AI Training vs. Inferencing: A Comparison of the Data Center Infrastructure Each Requires

In the past few years, AI has seized the spotlight, leading groundbreaking research and catalyzing transformative progress across many industries. As organizations at all scales continue to tackle complex challenges and unlock new possibilities, the ability to meet distinct infrastructural requirements for various AI workloads becomes increasingly crucial.

AI training and inferencing are two such workloads, each with distinct functions and requirements. While both play crucial roles and often function hand in hand, they entail different computational demands and necessitate varying infrastructural setups within the data center.

Understanding AI Training vs AI Inferencing

Before delving into the differences in data center infrastructure requirements, it’s essential to establish the distinguishing features between these two types of workloads.

AI Training

AI training involves feeding vast amounts of data into a designated machine learning algorithm to enable it to recognize patterns, make predictions, and execute specific tasks. As the algorithm is exposed to more data, it repeatedly refines its internal parameters to enhance the accuracy of its predictions based on the outcomes portrayed in the given dataset. This process is extremely demanding and requires extensive computational resources and time but is a crucial step in constructing robust and highly intelligent models.

An example of AI training can be found in the development of an autonomous vehicle. The vehicle must be able to effectively identify pedestrians crossing the road. To achieve this, the developers collect and feed the model extensive datasets containing images or sensor data capturing pedestrians on the road with different perspectives, lighting conditions, and environments. Through this process, the model undergoes training to recognize pedestrians and distinguish them from other objects on the road.

AI Inference

AI inference occurs after the model has been trained. During inferencing, the trained model applies its acquired knowledge to analyze new data, generating predictions or classifications. Compared to training, inferencing is generally less computationally intensive, focusing on efficiently executing the learned model architecture on individual or small batches of input data in real-time. This process is crucial for deploying AI solutions in various practical applications.

In the above example of autonomous driving, AI inferencing occurs as the autonomous vehicle navigates through the streets, relying on its pre-training model to guide decision-making. Using the vehicle’s sensors, the driving system can successfully identify a pedestrian crossing the street and adjust the vehicle’s speed to ensure the safety of the pedestrian as well as the vehicle’s passengers.

Differing Infrastructural Needs

AI Training Infrastructure Needs

Data centers hosting AI training house highly complex and intensive workloads and should deliver exceptional computing. Additionally, significant storage capacity is required to store the massive datasets used for training. Some particular things to look out for in an effective AI training platform include:

• Advanced Computational Power: Training workloads demand substantial computational resources to process large datasets and iteratively adjust model parameters. Effective AI servers should feature high-performance GPUs or specialized AI accelerators.
• Large Storage Capacity: Training datasets can be massive, requiring extensive storage capacity to store and access data efficiently. High-capacity storage SSDs or NVMe drives are necessary to handle the large quantity of data and minimize data access latency.
• High-Speed Interconnectivity: Efficient data movement and parallel processing capabilities are crucial for accelerating training times. High-speed interconnects enable efficient communication and performance.

Servers like the KR6288V2 with 8x NVIDIA HGX H100/A100 GPUs, 24x 2.5” SSD or up to 16x NVMe U.2, and lightning-fast CPU-to-GPU interconnect bandwidth would excel running AI training workloads.

AI Inference Infrastructure Needs

Compared to training, AI inferencing places less strain on computational resources but demands low latency and high throughput for real-time processing. Data centers supporting AI inferencing tasks often employ accelerators designed to execute inference tasks rapidly and efficiently, making them suitable for deployment in edge computing environments where latency is critical. Some features to look out for in an effective AI inference platform include:

• Low Latency: Inferencing workloads prioritize low latency and high throughput to efficiently process real-time data and make real-time decisions.
• Scalability: Data centers hosting inferencing workloads should be able to easily scale horizontally to handle varying levels of inference requests and accommodate demands for growth.
• Reliability and Support: Reliable hardware that minimizes downtime and dependable support for when issues may arise are essential to running inferencing workloads consistently without delays.

With its PCIe 5.0 architecture supporting high-throughput data transmission bandwidth up to 64GB/s and 300TB local high-speed storage, KR4268V2 delivers the rapid data access required for real-time AI inference.

AI training and inference are just two fundamental steps in AI development. As our world continues to push the boundaries of AI capabilities, data centers shoulder the responsibility of elevating advanced workloads and applications to new heights. As a server solutions provider, we at Aivres are dedicated to driving continuous innovation within the AI space to effectively enable ever-evolving workloads. By understanding unique infrastructure needs and leveraging advancements in hardware technologies, organizations can optimize their data center architectures to drive innovation and unlock the full potential of AI.