Not long ago, bare metal servers were considered outdated—a relic of an earlier era in computing. But fast forward to 2025, and they’re back in the spotlight, thanks in large part to artificial intelligence (AI) and high-performance computing (HPC). The raw power, isolation, and customizability of bare metal are proving essential for AI workloads, and DevOps teams are taking notice.
As organizations prioritize performance, cost control, and data sovereignty, bare metal offers a powerful alternative to traditional cloud VMs. Especially for workloads like deep learning, inference, and GPU-intensive training models, bare metal can outperform virtualized environments and provide greater transparency into performance.
In this blog, we explore how AI is driving the resurgence of bare metal, what this means for DevOps teams, and how to leverage bare metal effectively in your infrastructure strategy.
Why Bare Metal Is Making a Comeback
1. Performance Without Overhead
Unlike virtual machines, bare metal doesn’t have a hypervisor layer, which means no resource contention or virtualization tax. This is critical for:
- AI model training
- GPU-intensive tasks
- Real-time analytics
- Latency-sensitive applications
2. Custom Hardware Configurations
AI use cases often require:
- High memory-to-core ratios
- Specialized GPUs (NVIDIA A100, H100, etc.)
- NVMe storage
Bare metal gives DevOps teams the freedom to tailor hardware to specific workloads.
3. Predictable Cost and Performance
With fixed hardware, you get consistent throughput and latency—no noisy neighbors. This predictability is vital when optimizing ML pipelines and tuning performance.
4. Data Sovereignty and Security
For industries handling sensitive data (healthcare, finance, defense), bare metal offers physical isolation, stronger compliance alignment, and lower risk of shared infrastructure vulnerabilities.
AI Workloads That Benefit from Bare Metal
Bare metal is particularly well-suited for the following AI applications:
- Large Language Model (LLM) Training
- Computer Vision
- Autonomous Systems
- Speech Recognition and Synthesis
- Recommendation Engines
- AI-Powered Simulation and Modeling
These workloads typically demand high-performance GPUs, fast I/O, and minimal interruptions—a perfect match for bare metal environments.
How DevOps Teams Can Leverage Bare Metal
1. Infrastructure as Code for Bare Metal
Modern DevOps teams can manage bare metal just like cloud:
- Use Terraform, Pulumi, or Ansible to provision servers.
- Tools like Tinkerbell, MAAS, and Razor automate physical provisioning.
- Combine with GitOps workflows for deployment automation.
2. Kubernetes on Bare Metal
Run containerized workloads with the performance of metal:
- Bare metal Kubernetes offers better GPU scheduling and less overhead.
- Tools like MetalLB, Cilium, and Rancher simplify networking and cluster management.
3. Hybrid Cloud with Bare Metal Edge
Bare metal at the edge provides compute close to users and devices:
- Ideal for autonomous vehicles, retail analytics, and smart factories
- Edge-native platforms like Equinix Metal, Packet, and Ridge support global bare metal deployments
4. CI/CD for Hardware-Centric Workloads
Use CI/CD pipelines to deploy to bare metal environments:
- Integrate GitHub Actions, GitLab CI, or Jenkins for automated builds and tests
- Use hooks for hardware provisioning, BIOS/firmware checks, and OS installation
Comparing Bare Metal and Cloud VMs for AI Workloads
When evaluating infrastructure for AI, DevOps teams must weigh performance, cost, security, and scalability. Here's how bare metal servers compare to cloud virtual machines (VMs) in key categories:
Performance
- Bare Metal: Offers the highest performance since there’s no hypervisor layer. Ideal for compute-intensive tasks like AI model training and real-time analytics.
- Cloud VMs: Generally provide good performance, but virtualization introduces overhead and potential resource contention.
Cost
- Bare Metal: Typically comes with predictable pricing. You pay for dedicated hardware, which avoids surprise costs or billing spikes.
- Cloud VMs: Operate on a pay-as-you-go model, which is flexible but can result in unexpected charges during peak usage.
Customization
- Bare Metal: Provides full control over hardware configurations. You can specify GPU models, memory, storage, and networking to match AI workload needs.
- Cloud VMs: Limited to predefined instance types. Customization is minimal compared to physical servers.
Security
- Bare Metal: Offers physical isolation, reducing the risk of multi-tenant vulnerabilities. Preferred for sensitive workloads.
- Cloud VMs: Operate in multi-tenant environments, which can introduce security and compliance concerns depending on the application.
GPU Flexibility
- Bare Metal: Enables custom GPU configurations, including advanced setups like multi-GPU interconnects or dedicated PCIe lanes.
- Cloud VMs: Provide access to GPUs via fixed instance types, which may limit configuration flexibility.
Scalability
- Bare Metal: Hardware provisioning can take longer, which impacts scalability for dynamic workloads.
- Cloud VMs: Highly scalable with near-instant provisioning, ideal for bursty or unpredictable workloads.
Common Bare Metal Pitfalls and How to Avoid Them
1. Provisioning Delays
- Solution: Use APIs and IaC tools to automate setup
2. Underutilization
- Solution: Consolidate workloads and use container orchestration for efficiency
3. Monitoring and Logging
- Solution: Integrate with Prometheus, Grafana, and ELK Stack to maintain visibility
4. Scalability Limits
- Solution: Combine bare metal with cloud-native autoscaling in hybrid architectures
Bare Metal Providers to Watch in 2025
- DedicatedNodes: Custom bare metal with edge locations and GPU configurations
- Equinix Metal: API-driven global bare metal infrastructure
- OVHcloud: High-performance, cost-effective hardware in Europe and North America
- PhoenixNAP: Specialized GPU and AI-ready servers
- Oracle Cloud Infrastructure (OCI) Bare Metal: Integration with high-speed networking and block storage
The DevOps Mindset for Bare Metal
To succeed with bare metal, DevOps teams should:
- Embrace automation at every layer
- Treat infrastructure like code, even if it’s physical
- Monitor resource consumption rigorously
- Build modular, portable deployment workflows
- Stay on top of firmware, OS, and driver updates
Future Trends: AI + Bare Metal + Edge
- Composable Infrastructure: Dynamically assemble compute, storage, and GPU resources
- AI Workload Scheduling: Advanced schedulers optimize GPU clusters
- Liquid Cooling: Reduces power consumption and improves density
- AI on ARM Servers: Low-power, high-efficiency alternatives to x86-based hardware
AI isn’t just transforming software—it’s reshaping infrastructure priorities. As DevOps teams strive to meet growing demands for speed, scale, and intelligence, bare metal offers a compelling solution.
By combining the best of both worlds—cloud-like automation with physical performance control—bare metal is shedding its legacy image and becoming a strategic asset in the age of AI.
DevOps teams ready to embrace this evolution will gain a competitive edge in deploying smarter, faster, and more secure infrastructure.
Stay tuned as we explore the next frontier: where bare metal meets AI at the edge.
