Why Bare Metal Is the Unsung Hero of the AI Revolution in 2025

The progress of Artificial Intelligence (AI) is usually attributed to advanced algorithms and massive datasets. However, these achievements rely on robust computing systems that can efficiently manage complex workloads. Therefore, the performance of AI models not only depends on their algorithmic design but also on the infrastructure that supports them.

Although cloud and virtualized platforms receive most of the attention, bare metal servers play an equally important, yet less visible, role. They provide direct hardware access, complete resource control, and consistent high performance without the delay caused by virtualization layers.

Moreover, such environments are ideal for training large language models, processing extensive datasets, and running time-sensitive AI applications.

Consequently, bare metal provides the speed, reliability, and stability required for demanding AI workloads.

What Makes Bare Metal Essential for AI

Bare metal servers are physical machines dedicated to a single user or organization, providing a dedicated computing environment. They are not shared through virtualization, which allows full access to the hardware. Users can directly control the CPU, GPU, memory, storage, and network.

These servers support AI workloads that require speed, precision, and control. By removing virtualization layers, bare metal avoids performance loss from hypervisors and shared resources. This enables engineers to:

• Run large AI models without interference from other users.
• Configure and fine-tune systems for frameworks such as PyTorch or TensorFlow.
• Optimize GPU, memory, and I/O performance without delays.

Moreover, bare metal servers provide reliable performance, making them suitable for large language models, deep learning, and real-time analytics. These capabilities enable faster training, lower latency, and consistent results, making bare metal essential for AI workloads.

The Demands of AI Workloads

Modern AI tasks place significant demands on computing infrastructure. With the increasing size of models and the continuous growth in data, systems must maintain high throughput, stable performance, and minimal delays. These needs extend beyond processing speed and encompass efficient data handling, reliable communication between nodes, and predictable performance during extended training cycles.

Bare metal servers are designed to meet such requirements. They offer full GPU acceleration for parallel computation, NVMe storage for rapid data access, and low-latency networking for distributed workloads. This setup ensures steady and uninterrupted operation during intensive processing.

In comparison, virtualized environments often face latency variations and resource contention, which can reduce efficiency. Bare metal servers prevent these problems by providing consistent performance and direct hardware control. Therefore, they form a dependable and efficient base for modern AI infrastructure.

Bare Metal in AI Model Training

Training advanced AI models requires uninterrupted access to computing power and memory. Each training cycle involves millions of calculations and continuous data transfer. Any delay or fluctuation in resources can slow the process or affect model accuracy.

Bare metal servers address these needs directly. They offer unrestricted GPU access for faster computation, high I/O throughput for efficient data handling, and consistent performance without interference from virtualization. This setup reduces training time, improves scalability, and ensures uniform results across runs.

Moreover, predictable performance enables organizations to plan costs and manage energy use more efficiently. For these reasons, bare metal servers have become a preferred choice for large-scale AI model training and deployment.

Deployment Options: On-Premises vs Cloud

Bare metal servers are no longer limited to traditional on-premises deployments. Many providers now offer bare metal cloud hosting, which combines the high performance of dedicated hardware with the flexibility and scalability of the cloud. This setup enables organizations to deploy

workloads quickly; scale resources as needed and maintain complete control over hardware configurations.

For instance, Atlantic.Net provides bare metal cloud hosting equipped with NVIDIA GPUs, including H100 and L40S models, NVMe SSD storage, and high-speed networking. Their infrastructure is also compliance-ready, meeting standards such as HIPAA and PCI DSS. By combining performance with operational flexibility, this approach enables organizations to run demanding AI workloads efficiently and reliably on a scale.

Security and Compliance

Bare metal servers offer significant security advantages due to their physical isolation of resources. This means sensitive data faces fewer risks compared to virtualized environments, where shared resources can introduce vulnerabilities. Moreover, network isolation through VLANs or VRFs adds another layer of protection, preventing unauthorized access and enhancing system integrity.

In addition, bare metal infrastructures often comply with strict standards such as HIPAA, PCI DSS, and SOC 2, which ensure regulatory requirements are met. Consequently, this combination of physical isolation, network safeguards, and compliance significantly reduces the risk of cross-tenant vulnerabilities, making bare metal a dependable option for secure and reliable AI workloads.

Performance Benefits

The primary advantage of bare metal servers is their superior performance. By removing virtualization overhead, they deliver consistent, high-speed computing that enables AI engineers to optimize CPU, GPU, memory, and storage for their frameworks. High-throughput GPUs, NVMe storage, and low-latency networking ensure predictable, uninterrupted operation for large-scale workloads.

This enables efficient training of complex AI models while maintaining accuracy and repeatability. By supporting both stability and scalability, bare metal complements its security and deployment advantages, making it an essential component for demanding AI applications.

Choosing a Bare Metal Provider for AI

Selecting the right bare metal provider is essential for AI projects, as it impacts performance, security, and scalability. Organizations should consider several key factors.

First, hardware and GPU capability are critical, as modern CPUs and high-performance GPUs enable the efficient processing of large models and complex workloads. Compliance and security are also vital, particularly for sensitive or regulated data; providers that meet standards such as HIPAA, SOC 2, and PCI DSS offer the necessary protection.

Reliability and support help maintain continuous operation, with 24/7 technical assistance and robust service level agreements that reduce the risk of costly downtime. Additionally, network performance must be robust, featuring redundant and high-speed connectivity to ensure stability.

Key considerations are summarized below:

• Hardware and GPU capability: Modern CPUs and GPUs for fast and efficient processing.
• Compliance and security: HIPAA, SOC 2, PCI DSS standards for sensitive workloads.
• Reliability and support: 24/7 technical assistance and robust SLAs.
• Pricing transparency: Clear cost structures to avoid unexpected expenses.
• Network performance: Redundant, high-speed connectivity for stable operations.

Leading providers such as Atlantic.Net, IBM Cloud, AWS, Oracle Cloud, and OpenMetal each offer a mix of strengths across these areas, providing organizations with reliable options depending on workload size, compliance requirements, and budget.

Top Bare Metal Hosting Providers for AI Projects

1. Atlantic.Net

Atlantic.Net offers robust bare metal hosting solutions equipped with NVIDIA H100 and L40S GPUs, designed to meet the demanding requirements of AI workloads. Their infrastructure supports NVMe SSD storage and high-speed networking, ensuring optimal performance for tasks like large language model training and real-time inference.

The platform is compliant with HIPAA, PCI DSS, and SOC 2 standards, making it suitable for industries where security and data privacy are of the utmost importance. Users can configure CPU, RAM, and NVMe storage according to the demands of their AI workloads, providing flexibility to create an environment that matches specific frameworks and performance needs.

Advantages:

• Provides access to top-tier NVIDIA H100 and L40S GPUs, which are essential for training large models and demanding inference tasks.
• Offers a robust compliance framework, independently audited for HIPAA, PCI DSS, and SOC 2/3, ensuring data integrity and security.
• Allows for deep hardware customization, enabling users to specify exact CPU, RAM, and NVMe SSD storage configurations to match workload requirements.
• Features 24/7 U.S.-based support and a clear, predictable pricing model, which simplifies budgeting for long-term AI projects.

Ideal for:

• Healthcare organizations and MedTech startups handling sensitive patient data (PHI) for AI-driven diagnostics or research.
• Fintech and e-commerce companies that must maintain PCI DSS compliance while running fraud detection or personalization models.
• Any organization that prioritizes stringent security and regulatory adherence alongside high-end GPU performance.

2. IBM Cloud

IBM Cloud offers enterprise-grade bare metal servers equipped with a range of NVIDIA GPUs, including the H200 and L40S models. Their offerings are well-suited for AI applications that require high computational power and adhere to industry standards.

IBM Cloud’s infrastructure is designed for high resiliency and security, supporting AI, HPC, and visualization workloads. The platform offers flexible deployment options, including stand-alone servers and private cloud configurations, allowing organizations to tailor their infrastructure to specific needs.

Advantages:

• Features access to powerful, next-generation NVIDIA H200 and L40S GPUs, tailored for enterprise-scale AI and HPC.
• Built on an infrastructure with high resiliency and robust security, designed to meet complex enterprise-grade reliability standards.
• Offers flexible deployment models, including stand-alone servers and seamless integration into VPCs (Virtual Private Clouds).
• Strong support for hybrid cloud strategies, allowing businesses to connect on-premises data centers with cloud-based bare metal for consistent performance.

Ideal for:

• Large-scale enterprises that require a stable, secure, and highly reliable platform for mission-critical AI applications.
• Research and academic institutions running High-Performance Computing (HPC) and AI workloads that demand significant, sustained computational power.
• Businesses that need to integrate bare metal performance directly into their existing IBM Cloud hybrid or private cloud environments.

3. AWS (Amazon Web Services)

AWS offers bare metal EC2 instances, providing direct access to physical hardware while integrating with the broader AWS ecosystem. Their GPU offerings include the NVIDIA A100 and T4 models, which are suitable for various AI and machine learning tasks.

The platform holds certifications such as HIPAA, PCI-DSS, SOC 2, and GDPR. Under the shared responsibility model, AWS secures the underlying infrastructure, while clients are responsible for ensuring the security of their data and applications within the cloud.

Advantages:

• Enables direct integration with the full AWS ecosystem, allowing bare metal instances to natively access services like Amazon S3, VPC, and Amazon SageMaker.
• Provides direct, non-virtualized access to the underlying server’s processor and memory, eliminating hypervisor overhead for maximum performance.
• Offers proven NVIDIA H100, A100, and T4 GPUs, providing a solid, well-supported hardware base for a variety of machine learning and inference tasks.
• Leverages AWS’s extensive global network and availability zones for high availability and low-latency data access.

Ideal for:

• Organizations already deeply embedded in the AWS ecosystem that need to optimize performance for specific, resource-intensive workloads.
• Applications that require native, high-speed access to large datasets stored in Amazon S3 or that are part of a broader AWS data pipeline.
• Teams using Amazon SageMaker or other AWS ML services that want to add a high-performance, bare metal component for training or inference.

4. Oracle Cloud Infrastructure (OCI)

OCI provides bare metal instances with NVIDIA GPUs, including A100 and H100 models, optimized for AI and high-performance computing workloads. Their infrastructure supports RDMA-based networking and scalable superclusters.

OCI’s platform is compliant with GDPR, HIPAA, and SOC standards. The platform offers flexible configurations and professional services, allowing organizations to tailor their infrastructure to specific requirements.

Advantages:

• Provides elite NVIDIA A100 and H100 GPUs at what is often a highly competitive price point, offering strong price-performance.
• Features RDMA (Remote Direct Memory Access) cluster networking, which provides ultra-low latency and high bandwidth for rapid communication between nodes.
• Designed specifically for building large-scale GPU superclusters, allowing for efficient distributed training of massive foundation models.
• Offers a secure, enterprise-focused cloud environment with strong compliance certifications (GDPR, HIPAA, SOC).

Ideal for:

• High-Performance Computing (HPC) and AI research teams that need to build and scale massive, multi-node GPU clusters.
• Large-scale distributed training for foundation models or complex simulations where inter-node latency is a critical bottleneck.
• Organizations looking for the best raw performance-per-dollar ratio for high-end H100 or A100 GPU instances.

5. OpenMetal

OpenMetal offers on-demand private cloud and bare metal services built on OpenStack and Ceph, providing flexibility and control for AI projects. Their platform supports NVIDIA A100 and H100 GPUs, suitable for large-scale AI deployments.

OpenMetal’s infrastructure is compliant with HIPAA, SOC 2, and other relevant standards, ensuring the security and privacy of sensitive workloads. The platform offers complete control over hardware configurations, enabling organizations to tailor their infrastructure to specific AI tasks.

Advantages:

• Built on open-source platforms (OpenStack and Ceph), providing full transparency and eliminating proprietary vendor lock-in.
• Offers the flexibility of an on-demand private cloud combined with the raw performance of bare metal hardware.
• Provides access to NVIDIA A100 and H100 GPUs, allowing for high-performance AI workloads on a customizable, open platform.
• Grants complete root-level control over the hardware and cloud environment, ideal for complex, custom configurations.

Ideal for:

• Service providers (MSPs) and enterprises that want to build their own AI cloud using open-source standards.
• Organizations that require deep customization and control over their infrastructure, beyond what is offered by hyperscale providers.
• Teams with OpenStack expertise looking to deploy on-demand bare metal to avoid vendor lock-in and maintain a fully transparent stack.

Table 1: Comparison of different bare metal hosting providers

The Future of Bare Metal in AI

Choosing the right provider depends on the goals and priorities of each AI project. For compliance-focused environments, Atlantic.Net and IBM Cloud offer strong regulatory certifications and enterprise-grade reliability. Those interested in cost-effective GPU hosting can benefit from OCI, which provides competitive pricing for NVIDIA A100 instances. Meanwhile, OpenMetal is better suited for teams that need complete control and customization, while AWS remains ideal for organizations seeking seamless integration within a broad cloud ecosystem.

AI models are continually expanding in scale and complexity, making bare-metal infrastructure vital for supporting their performance, efficiency, and security requirements. By combining the reliability of dedicated hardware with the scalability offered by modern cloud platforms, bare metal provides a robust foundation for demanding AI workloads. While cloud platforms get most of the attention, bare metal quietly powers the backbone of modern AI, supporting the performance and efficiency needed for breakthroughs in 2025 and beyond.

Final Thoughts

Bare metal servers have become essential for AI in 2025 because they provide the performance, control, and reliability required for large-scale workloads. By offering direct access to dedicated CPUs, GPUs, memory, and storage, they eliminate virtualization overhead, which in turn enables faster model training, real-time inference, and consistent performance. In addition, security and compliance are reinforced through physical and network isolation, helping organizations meet standards such as HIPAA, SOC 2, and PCI DSS.

When selecting a provider, organizations must carefully consider GPU options, customization capabilities, support, network stability, and pricing. Providers like Atlantic.Net, IBM Cloud, AWS, OCI, and OpenMetal each offer unique advantages, enabling teams to tailor their infrastructure to their specific AI requirements. Therefore, bare metal remains a reliable, efficient, and secure solution for running demanding AI workloads at scale.