The evolution of technologies related to artificial intelligence (AI) has led to an increasing demand for computing power, essential for managing the training, learning, and inferencing of machine learning and deep learning models. In this context, GPUs (Graphics Processing Units) have established themselves as fundamental components, thanks to their ability to perform large-scale parallel computations extremely efficiently. With the upcoming releases of Windows Server 2025 and Azure Stack HCI 24H2, Microsoft introduces significant innovations that enable companies to fully harness the potential of GPUs not only in AI but beyond. These advanced new features simplify hardware resource management and provide an optimized platform for developing and deploying AI solutions on a large scale. In this article, we will explore the importance of GPUs in the AI ecosystem and analyze how the new versions of Windows Server 2025 further enhance these capabilities, transforming how companies tackle the challenges and opportunities presented by AI.
Computing Power and GPU Optimization for Deep Learning on Virtual Infrastructures
Deep learning, an advanced branch of artificial intelligence that leverages deep artificial neural networks, requires a vast amount of computing power to function effectively. Training these models involves processing large volumes of data through multiple layers of interconnected nodes, each performing complex mathematical operations. While traditional CPUs are highly powerful in sequential data processing, they are not optimized to handle a large number of parallel operations, as required by deep learning models.
In this context, GPUs (Graphics Processing Units) are particularly well-suited due to their ability to execute thousands of operations simultaneously. This makes GPUs ideal for training deep learning models, especially complex ones like convolutional neural networks (CNNs), which are widely used in image recognition. For example, training a CNN on a large dataset could take weeks on a CPU, while with the help of a GPU, the time required can be drastically reduced to just days or even hours, depending on the model’s complexity and the dataset’s size.
With the imminent release of Windows Server 2025 and Azure Stack HCI 24H2, Microsoft will offer its customers the ability to allocate an entire GPU’s capacity to a virtual machine (VM), which can run both Linux and Windows Server operating systems within a fault-tolerant cluster, thanks to Discrete Device Assignment (DDA) technology. This means that critical AI workloads for businesses can be reliably executed on a VM within a cluster, ensuring that, in the event of an unexpected failure or planned migration, the VM can be restarted on another node in the cluster using the GPU available on that node.
Microsoft recommends working closely with OEM (Original Equipment Manufacturer) partners and independent GPU hardware manufacturers (IHV) to plan, order, and configure the necessary systems to support the desired workloads with the right configurations and software. Additionally, if GPU acceleration via DDA is desired, it is advisable to consult with OEM and IHV partners to obtain a list of GPUs compatible with DDA. To ensure the best possible performance, Microsoft also suggests creating a homogeneous configuration for GPUs across all servers in the cluster. A homogeneous configuration implies installing the same GPU model and configuring the same number of partitions on all GPUs in the cluster’s servers. For example, in a cluster consisting of two servers each with one or more GPUs, all GPUs should be of the same model, brand, and size, and the number of partitions on each GPU should be identical.
Scalability and Flexibility of GPUs in AI Computing Architectures
In addition to their extraordinary computational speed, GPUs also offer significant advantages in terms of scalability, a crucial factor in modern AI computing architectures. Often, the datasets used to train AI models are so vast that they exceed the computational capabilities of a single processor. In these cases, GPUs allow the workload to be distributed across multiple computing units, ensuring high operational efficiency and enabling the simultaneous processing of enormous amounts of data.
Another critical aspect of GPUs is their flexibility in handling a variety of workloads, ranging from real-time inference, used for example in speech recognition applications, to the training of complex models that require weeks of intensive computation. This versatility makes GPUs an indispensable tool not only for advanced research centers but also for commercial applications that require high performance on a large scale.
GPU Partitioning: Maximizing Efficiency and Resource Utilization
One of the most significant innovations in the field of GPUs is the concept of GPU Partitioning, which is the ability to divide a single GPU into multiple virtual partitions, each of which can be dedicated to different workloads. This technique is crucial for optimizing GPU resources, as it maximizes operational efficiency while minimizing waste. In the context of artificial intelligence, where computational requirements can vary significantly depending on the models used, GPU Partitioning offers the flexibility to dynamically allocate portions of the GPU to various tasks, such as training machine learning models, real-time inference, or other parallel operations. This approach is particularly advantageous in data centers, as it allows multiple users or applications to share the same GPU resources without compromising overall system performance.
The introduction of GPU Partitioning not only improves the flexibility and scalability of computing infrastructures but also helps reduce operational costs by avoiding the need to purchase additional hardware when not strictly necessary. Additionally, this technology promotes a more balanced use of resources, preventing situations of GPU overload or underutilization, contributing to more sustainable and efficient management of AI-related operations.
With the release of Windows Server 2025 Datacenter, Microsoft has integrated and enhanced support for GPU Partitioning, allowing customers to divide a supported GPU into multiple partitions and assign them to different virtual machines (VMs) within a fault-tolerant cluster. This means that multiple VMs can share a single physical GPU, with each receiving an isolated portion of the GPU’s capabilities. For example, in the retail and manufacturing sectors, customers can perform inferences at the edge using GPU support to obtain rapid results from machine learning models, results that can be used before the data is sent to the cloud for further analysis or continuous improvement of ML models.
GPU Partitioning utilizes the Single Root IO Virtualization (SR-IOV) interface, which provides a hardware-based security boundary and ensures predictable performance for each VM. Each VM can only access the GPU resources dedicated to it, with secure hardware partitioning preventing unauthorized access by other VMs.
Another significant development concerns live migration capability for VMs using GPU Partitioning. This allows customers to balance critical workloads across various cluster nodes and perform hardware maintenance or software updates without interrupting VM operations. During a planned or unplanned migration, the VMs can be restarted on different nodes within the cluster, using available GPU partitions on those nodes.
Finally, Microsoft has made the Windows Administration Center (WAC) available to configure, use, and manage VMs that leverage virtualized GPUs, both in standalone configurations and in failover clusters. The WAC centralizes the management of virtualized GPUs, significantly simplifying administrative complexity.
Innovations and Future Prospects
The future of GPUs in artificial intelligence looks extremely promising. With the increasing complexity of AI models and the growing demand for solutions capable of leveraging real-time AI, the parallel computing power offered by GPUs will become increasingly essential. In particular, their ability to perform a large number of simultaneous operations on vast datasets makes them an indispensable component in cloud solutions.
The significant innovations in GPUs, supported by the upcoming releases of Windows Server 2025 and Azure Stack HCI 24H2, are the result of ongoing and close collaboration between Microsoft and NVIDIA. Microsoft Azure handles some of the world’s largest workloads, pushing CPU and memory capabilities to the limit to process enormous volumes of data in distributed environments. With the expansion of AI and machine learning, GPUs have become a key component of cloud solutions as well, thanks to their extraordinary ability to perform large-scale parallel operations. Windows Server 2025 will bring many benefits to the GPU sector as well, further enhancing features related to storage, networking, and the scalability of computing infrastructures.
Conclusions
The importance of GPUs in the field of artificial intelligence is set to grow exponentially, thanks to their ability to process large volumes of data in parallel with efficiency and speed. The innovations introduced in Windows Server 2025 and Azure Stack HCI 24H2 represent a significant step toward optimizing computing infrastructures, providing companies with advanced tools to manage and fully exploit GPU resources. These developments not only enhance the computing power necessary for AI but also introduce greater flexibility and scalability, essential for addressing future challenges. With the adoption of technologies like GPU Partitioning and support for live VM migration, Microsoft demonstrates its leadership in providing solutions that not only improve performance but also enhance the reliability and sustainability of AI-related business operations. The future prospects see GPUs playing an increasingly crucial role, not only in data centers but also in edge and cloud applications, ensuring that technological innovation continues to drive the evolution of AI across all sectors.
Useful References
- Windows Server 2025: the arrival of a new era of innovation and security for server systems
- Partition and share GPUs with virtual machines on Hyper-V | Microsoft Learn
- Deploy graphics devices by using Discrete Device Assignment | Microsoft Learn
- Use GPUs with clustered VMs on Hyper-V | Microsoft Learn
- Plan for GPU acceleration in Windows Server | Microsoft Learn
