7/23/2025

Using Windows Server 2025 for Artificial Intelligence and Machine Learning:

Hardware and Software Requirements: Windows Server 2025, released on November 1, 2024, is a robust platform designed to handle demanding workloads, including artificial intelligence (AI), machine learning (ML), natural language processing (NLP), and large language model (LLM) training. Its advanced features, such as enhanced Hyper-V virtualization, GPU partitioning, and Azure Arc integration, make it an excellent choice for organizations aiming to build AI-driven solutions on-premises or in hybrid cloud environments. This article provides a comprehensive guide on how to leverage Windows Server 2025 Standard for AI, ML, NLP, and LLM model training, detailing the necessary hardware and software requirements to ensure optimal performance.Why Windows Server 2025 for AI and ML?Windows Server 2025 Standard is a versatile operating system that supports traditional server workloads and modern AI applications. Its key features for AI include:

  1. Hyper-V Enhancements: Windows Server 2025 supports up to 2,048 virtual processors per virtual machine (VM), enabling scalable AI workloads across distributed environments. The improved GPU partitioning (GPU-P) allows multiple VMs to share GPU resources, which is critical for AI and ML tasks.
  2. Azure Arc Integration: Azure Arc extends cloud capabilities to on-premises servers, simplifying management and enabling hybrid cloud AI deployments.
  3. NVMe Storage Support: With up to 90% more IOPS (input/output operations per second), Windows Server 2025 optimizes data access for large datasets used in AI and ML.
  4. Windows Machine Learning (Windows ML): This high-level API supports hardware-accelerated ML model inference, making it easier to deploy AI models on Windows Server.
  5. Security Features: Enhanced Active Directory, SMB over QUIC, and hypervisor-protected code integrity (HVPT) ensure secure AI workloads, protecting sensitive data and models.

These features make Windows Server 2025 Standard a powerful platform for AI, ML, NLP, and LLM training, particularly for organizations that prefer on-premises infrastructure or need to integrate with Azure for hybrid scenarios.Hardware Requirements for AI, ML, NLP, and LLM Model Training AI and ML workloads, especially those involving NLP and LLMs, are computationally intensive and require robust hardware to handle data preprocessing, model training, and inference. Below are the recommended hardware specifications for running these workloads on Windows Server 2025 Standard.

1. Central Processing Unit (CPU): The CPU manages general-purpose tasks, such as data preprocessing, model architecture design, and VM operations. While GPUs dominate AI training, a powerful CPU is essential for efficient system management and non-GPU tasks like optical character recognition (OCR) or data cleaning.

  1. Minimum Requirement: A 64-bit CPU with a minimum clock speed of 1.4 GHz, supporting NX, DEP, CMPXCHG16b, LAHF/SAHF, PrefetchW, Second Level Address Translation (EPT or NPT), POPCNT, and SSE4.2. A 12-core, 24-thread processor, such as the AMD Ryzen 9 7950X or Intel Core i9-14900K, is suitable for basic AI tasks.
  2. Recommended: A server-grade CPU like the AMD EPYC 9254 or Intel Xeon Scalable (5th Generation) with 16+ physical cores and hyper-threading. These CPUs offer high core counts for parallel processing and sufficient PCIe lanes to support multiple GPUs. For LLM training, which involves massive datasets, opt for 32–64 cores to handle concurrent tasks efficiently.
  3. Why It Matters: High core counts and clock speeds (3.5 GHz or higher) accelerate data preprocessing and model orchestration. CPUs with AVX-512 support or integrated AI accelerators (e.g., Intel AMX) further enhance performance for matrix operations in deep learning.

2. Graphics Processing Unit (GPU): GPUs are the backbone of AI and ML workloads, particularly for training deep learning models and LLMs, due to their ability to perform parallel computations.

  1. Minimum Requirement: A CUDA-compatible NVIDIA GPU with at least 16 GB of VRAM, such as the NVIDIA RTX 3080 Ti. This is sufficient for small-scale ML tasks or development environments.
  2. Recommended: Enterprise-grade GPUs like the NVIDIA A100 (40–80 GB VRAM), RTX 6000 Ada (48 GB VRAM), or RTX 5090 (24 GB VRAM) for production-grade AI, NLP, and LLM training. For large-scale LLMs, multiple GPUs with NVLink for high-speed interconnects are ideal to handle massive model sizes and datasets.
  3. Why It Matters: VRAM is critical for AI tasks, as models and datasets must fit into GPU memory during training. For example, training a transformer-based LLM like BERT or GPT requires substantial VRAM (24 GB or more) to avoid performance bottlenecks. NVIDIA GPUs dominate due to their compatibility with frameworks like TensorFlow and PyTorch, though AMD Instinct GPUs are emerging as alternatives.

3. Memory (RAM): AI workloads, especially LLMs, are memory-intensive due to large datasets and concurrent processes.

  1. Minimum Requirement: 32 GB of DDR5 RAM for basic ML tasks or development.
  2. Recommended: 64–128 GB of ECC (Error-Correcting Code) DDR5 RAM with speeds of 4800 MHz or higher. For LLM training, 256 GB or more may be necessary to handle large datasets and multi-model operations.
  3. Why It Matters: ECC memory ensures data integrity, which is crucial for critical AI applications. High RAM capacity and speed reduce latency during data preprocessing and model training, especially when working with large feature spaces like high-resolution images or text corpora.

4. Storage: Fast and high-capacity storage is essential for managing large datasets, model checkpoints, and backups.

  1. Minimum Requirement: A 1 TB NVMe SSD for fast data access and low latency.
  2. Recommended: 2–4 TB NVMe SSDs for primary storage, supplemented by 8 TB or larger HDDs for archiving. RAID configurations (e.g., RAID 1 or 5) provide redundancy and performance optimization.
  3. Why It Matters: NVMe SSDs offer high throughput and low latency, critical for reading and writing large datasets during training. Windows Server 2025’s improved NVMe support delivers up to 90% more IOPS, making it ideal for AI workloads.

5. Power Supply Unit (PSU): AI systems with high-performance GPUs and CPUs are power-intensive.

  1. Minimum Requirement: A 1000W PSU, 80 PLUS Gold or Platinum certified.
  2. Recommended: A 1200–1600W PSU to support multiple GPUs and high-core-count CPUs, ensuring stability during sustained workloads.
  3. Why It Matters: GPUs like the NVIDIA RTX 5090 (575W TDP) or A100 (400W TDP) consume significant power, especially during 24/7 training tasks. A high-efficiency PSU reduces energy costs and ensures system reliability.

6. Networking: High-speed networking is necessary for distributed training, remote collaboration, and cloud integration.

  1. Minimum Requirement: A 1 Gbps Ethernet connection.
  2. Recommended: 10 Gbps Ethernet or InfiniBand for large-scale distributed training and fetching datasets from cloud storage.
  3. Why It Matters: Fast networking reduces data transfer times, which is critical for multi-node AI clusters or hybrid cloud setups with Azure Arc.


Software Requirements for AI, ML, NLP, and LLM Model Training:

To effectively utilize Windows Server 2025 Standard for AI workloads, you need a combination of operating system configurations, AI frameworks, and development tools. Below are the key software components.1. Operating System Configuration

  1. Windows Server 2025 Standard: This edition supports up to two Hyper-V VMs, making it suitable for small to medium-sized AI deployments. Install the Server Core option for headless deployments to reduce resource overhead or the Desktop Experience for a GUI-based setup if preferred.
  2. Hyper-V Configuration: Enable Hyper-V for virtualization to run multiple AI workloads in isolated VMs. Configure GPU partitioning to share GPU resources among VMs, which is particularly useful for training multiple models simultaneously.
  3. Windows Admin Center: Use this browser-based tool to manage servers, VMs, and storage, streamlining AI infrastructure management.
  4. Azure Arc: Enable Azure Arc for hybrid cloud capabilities, allowing seamless integration with Azure Machine Learning for model training and deployment.

2. AI and ML Frameworks:

The following frameworks are essential for building, training, and deploying AI models:

  1. TensorFlow: Google’s open-source framework, widely used for deep learning and NLP tasks. It supports GPU acceleration and is compatible with Windows Server.
  2. PyTorch: Facebook’s framework, known for its dynamic computational graph, is ideal for research and LLM training. It offers excellent Windows support.
  3. Keras: A high-level API running on TensorFlow, providing a user-friendly interface for rapid prototyping.
  4. ONNX Runtime: A cross-platform runtime for executing ML models, optimized for Windows Server 2025 via Windows ML. It supports CPUs, GPUs, and NPUs for hardware-accelerated inference.
  5. Windows ML: This built-in API enables hardware-accelerated model inference using ONNX models. It supports AMD, Intel, NVIDIA, and Qualcomm hardware, making it versatile for AI deployment.

3. Programming Languages and Tools

  1. Python: The primary language for AI development, supported by libraries like NumPy, Pandas, and Scikit-learn for data preprocessing and model building.
  2. Visual Studio Code: Use with the AI Toolkit for model conversion, quantization, and optimization. It integrates with Windows ML for seamless deployment.
  3. PowerShell: Automate server management tasks, such as configuring VMs or monitoring resources, to streamline AI workflows.

4. Additional Tools

  1. NVIDIA CUDA Toolkit: Required for NVIDIA GPU acceleration, ensuring compatibility with TensorFlow and PyTorch.
  2. Docker: Use Docker containers to deploy AI frameworks and models, simplifying environment setup and scalability.
  3. Prometheus and Grafana: Monitor system resources and performance during AI training to optimize resource utilization.
  4. Azure Machine Learning: For hybrid setups, use Azure Machine Learning to fine-tune and deploy models, leveraging Azure’s model catalog and REST APIs.


Setting Up Windows Server 2025 for AI Workloads:

Follow these steps to configure Windows Server 2025 Standard for AI, ML, NLP, and LLM training:

  1. Install Windows Server 2025 Standard:
  2. Choose the Server Core or Desktop Experience installation option based on your needs.
  3. Ensure the system meets the minimum hardware requirements (1.4 GHz 64-bit CPU, 32 GB RAM, 32 GB storage).
  4. Enable Hyper-V:
  5. Install the Hyper-V role via Windows Admin Center or PowerShell (Install-WindowsFeature -Name Hyper-V -IncludeManagementTools).
  6. Configure Generation 2 VMs (default in Windows Server 2025) for better performance and security.
  7. Set Up GPU Partitioning:
  8. Use the Hyper-V Manager to enable GPU-P, allowing VMs to share GPU resources.
  9. Install NVIDIA drivers and the CUDA Toolkit to enable GPU acceleration.
  10. Install AI Frameworks:
  11. Use Python’s package manager (pip) to install TensorFlow (pip install tensorflow), PyTorch (pip install torch), and other dependencies.
  12. Configure ONNX Runtime for model inference (pip install onnxruntime).
  13. Optimize Storage:
  14. Configure NVMe SSDs for primary storage and set up RAID for redundancy.
  15. Use Storage Spaces Direct for software-defined storage to handle large datasets.
  16. Enable Azure Arc:
  17. Follow the Azure Arc Setup wizard to connect your server to Azure, enabling hybrid cloud features and hotpatching for minimal downtime.
  18. Monitor and Scale:
  19. Use Prometheus and Grafana to monitor CPU, GPU, and memory usage.
  20. Scale your setup by adding GPUs or nodes as your AI workloads grow.

Best Practices for AI on Windows Server 2025

  1. Optimize GPU Usage: Ensure VRAM is sufficient for your models. For LLMs, use multi-GPU setups with NVLink to combine VRAM.
  2. Fine-Tune Models: Use Azure Machine Learning or local tools like LoRA to fine-tune models for specific tasks, improving accuracy and efficiency.
  3. Secure Your Environment: Leverage Active Directory and SMB hardening to protect sensitive AI data and models.
  4. Regular Backups: Use tools like Windows Server Backup or Azure Backup to protect datasets and model checkpoints.
  5. Energy Efficiency: Choose energy-efficient CPUs and GPUs (e.g., NVIDIA A100 with lower TDP) to reduce operational costs.


Windows Server 2025 Standard is a powerful platform for AI, ML, NLP, and LLM model training, offering robust virtualization, GPU support, and hybrid cloud integration. By investing in high-performance hardware—such as AMD EPYC or Intel Xeon CPUs, NVIDIA A100 or RTX 6000 Ada GPUs, 128 GB+ ECC RAM, and NVMe SSDs—and configuring essential software like TensorFlow, PyTorch, and Windows ML, organizations can build scalable and secure AI infrastructure. With Azure Arc and Windows Admin Center, managing and scaling AI workloads becomes seamless, making Windows Server 2025 an ideal choice for modern AI-driven enterprises.