If you’re wondering how an AI server is different from an AI workstation, you’re not the only one. Assuming strictly AI use cases with minimal graphics workload, obvious differences can be minimal to none. You can technically use one as the other. However, the results from each will be radically different depending on the workload each is asked to perform. For this reason, it’s important to clearly understand the differences between AI servers and AI workstations.
Setting AI aside for a moment, servers in general tend to be networked and are available as a shared resource that runs services accessed across the network. Workstations are generally intended to execute the requests of a specific user, application, or use case.
Can a workstation act as a server, or a server as a workstation? The answer is “yes,” but ignoring the design purpose of the workstation or server does not usually make sense. For example, both workstations and servers can support multithreaded workloads, but if a server can support 20x more threads than a workstation (all else being equal), the server will be better suited for applications that create many threads for a processor to simultaneously crunch.
Servers are optimized to scale in their role as a network resource to clients. Workstations are usually not optimized for massive scale, sharing, parallelism, and network capabilities.
Specific differences: Servers and workstations for AI
Servers often run an OS that is designed for the server use case, while workstations run an OS that is intended for workstation use cases. For example, consider Microsoft Windows 10 for desktop and individual use, whereas Microsoft Windows Server is run on dedicated servers for shared network services.
The principle is the same for AI servers and workstations. The majority of AI workstations used for machine learning, deep learning, and AI development are Linux-based. The same is true for AI servers. Because the intended use of workstations and servers is different, servers can be equipped with processor clusters, larger CPU and GPU memory resources, more processing cores, and greater multithreading and network capabilities.
Note that because of the extreme demands placed on servers as a shared resource, there is generally an associated greater demand on storage capacity, flash storage performance, and network infrastructure.
The GPU: An essential ingredient
The GPU has become an essential element in modern AI workstations and AI servers. Unlike CPUs, GPUs have the ability to increase the throughput of data and number of concurrent calculations within an application.
GPUs were originally designed to accelerate graphics rendering. Because GPUs can simultaneously process many pieces of data, they have found new modern uses in machine learning, video editing, autonomous driving, and more.
Although AI workloads can be run on CPUs, the time-to-results with a GPU may be 10x to 100x faster. The complexity of deep learning in natural language processing, recommender engines, and image classification, for example, benefits greatly from GPU acceleration.
Performance is needed for initial training of machine learning and deep learning models. Performance is also mandatory when real-time response (as for conversational AI) is running in inference mode.
It’s important that AI servers and workstations work seamlessly together within an enterprise–and with the cloud. And each has a place within an enterprise organization.
In the case of AI servers, large models are more efficiently trained on GPU-enabled servers and server clusters. They can also be efficiently trained using GPU-enabled cloud instances, especially for massive datasets and models that require extreme resolution. AI servers are often tasked to operate as dedicated AI inferencing platforms for a variety of AI applications.
Individual data scientists, data engineers, and AI researchers often use a personal AI or data science workstation in the process of building and maintaining AI applications. This tends to include data preparation, model design, and preliminary model training. GPU-accelerated workstations make it possible to build complete model prototypes using an appropriate subset of a large dataset. This is often done in hours to a day or two.
Certified hardware compatibility along with seamless compatibility across AI tools is very important. NVIDIA-Certified Workstations and Servers provide tested enterprise seamlessness and robustness across certified platforms.