Solutions Engineer

Choosing between bare metal and traditional virtual machines (VMs) can dramatically affect how efficiently you fine-tune AI models.

Bare metal gives you direct access to hardware for maximum performance. Traditional VMs trade some of that performance for ease of management and greater flexibility.

**The right choice depends on your fine-tuning workload and infrastructure priorities.**

Developers need infrastructure that scales without sacrificing speed or control—especially as AI adoption accelerates. The [global AI market](https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-market) hit $279.22 billion in 2024 and is expected to grow at a 35.9% CAGR through 2030\.

That’s where [RunPod](https://www.runpod.io) comes in. Designed for AI, RunPod automates GPU and TPU provisioning based on actual usage, giving you the power of bare metal with the convenience of a cloud platform.

## What Are the Differences Between Bare Metal and Traditional VMs?

Bare metal and traditional virtual machines (VMs) differ in how they handle resource access, performance, and scalability. All of which affect how you fine-tune AI models.

Briefly:

* **Bare metal servers** give you direct access to physical hardware. There’s no virtualization layer, so you control the full machine, including GPU, CPU, memory, and storage.  
* **Traditional VMs** run on top of a hypervisor that splits hardware resources across multiple VMs. This allows more flexibility but adds some performance overhead and limits resource isolation.

Read on to learn more about the differences between the two.

### Compute Performance

Bare metal offers consistent, high-throughput performance with no virtualization overhead. That’s especially useful when fine-tuning large models where full GPU access and speed matter.

Traditional VMs introduce a small (around 5 to 10%) performance drop due to the hypervisor.

For lighter or more flexible tasks, this trade-off may be acceptable. But for intensive training jobs, [bare metal](https://blog.runpod.io/introducing-bare-metal-dedicated-gpu-servers-with-maximum-control-and-savings/) typically delivers better results.

### Scalability and Flexibility

Traditional VMs scale quickly and easily. You can spin up or down virtual instances in minutes, making them ideal for experimentation or elastic AI workloads. They’re also easier to migrate across data centers or cloud environments.

Bare metal scales more slowly. Provisioning physical servers takes time, which can delay fast-moving projects. Serverless GPU platforms like RunPod help bridge that gap by [automating scaling](https://www.runpod.io/instant-clusters) without manual provisioning.

### Infrastructure Use Cases

Bare metal is best for high-performance workloads, large model training, or scenarios requiring deep customization and full hardware control.

Traditional VMs are ideal when flexibility, cost-efficiency, and rapid deployment are more important than absolute performance.

Many teams use a mix of both, training on bare metal while using VMs or [serverless GPUs](https://www.runpod.io/articles/top-serverless-gpu-clouds) for preprocessing, experimentation, or inference.

## Key Differences Between Bare Metal Servers and Traditional VMs for AI Fine-Tuning

Fine-tuning AI models pushes infrastructure to its limits, especially when dealing with large language models (LLMs), complex embeddings, or massive datasets. Choosing between bare metal and virtual machines affects how fast, how efficiently, and how affordably you can train.

Here’s how the two options compare across four core dimensions.

### Computational Performance

Bare metal typically delivers 15 to 20% more compute performance than VMs by removing the virtualization layer. This includes improvements in raw processing speed, measured in [floating-point operations per second (FLOPS)](https://www.runpod.io/ai-faq/what-is-the-flops-performance-of-the-nvidia-h100-gpu), which directly impacts how fast you can train large models. This results in lower latency and more consistent GPU and TPU access, which is ideal for training large models without performance bottlenecks.

Pairing bare metal with the [best GPUs for AI workloads](https://www.runpod.io/ai-faq/what-are-the-best-gpus-for-running-ai-models) can further boost training efficiency.

Virtual machines introduce overhead through the hypervisor but offer flexibility in resource allocation. For experimentation or dynamic scaling, this trade-off may be worth it. Balancing throughput and latency is essential when scaling.

If you're unsure which hardware fits your needs, RunPod’s [GPU comparison tool](https://www.runpod.io/compare/gpus) can help you evaluate based on memory, cores, and price.

### I/O and GPU Utilization

Bare metal gives you full access to underlying hardware, including high-speed SSDs and NVMe drives, which is critical for streaming large datasets. You also gain exclusive control of the GPU, with no memory segmentation or shared overhead. That’s a major advantage when using large-memory GPUs like the [AMD MI250](https://www.runpod.io/gpu/mi250) for training models with high VRAM requirements.

Traditional VMs, by contrast, may introduce GPU latency and throttle I/O bandwidth during peak demand. This can impact model performance in real-time training or inference scenarios where hardware consistency matters.

### Cost Efficiency

Bare metal often comes with higher upfront costs but may reduce long-term spend on sustained workloads. Managed services typically start around [$199 per month](https://www.runpod.io/gpu-bare-metal-server), not including high-end GPU add-ons. If you're running long training jobs, this pricing can pay off.

Traditional VMs use a pay-as-you-go model that aligns better with variable or short-term workloads. Cloud providers charge by the hour, making it possible to run basic jobs for pennies. For more advanced use cases, platforms like RunPod offer [cost-effective GPU pricing](https://www.runpod.io/ppc/cost-effective) at competitive rates.

To optimize spend:

* Use bare metal for persistent workloads  
* Use VMs or spot instances for burst tasks  
* Automate scaling and idle shutdowns  
* Use containers to maximize portability and efficiency

More details are available on [RunPod’s pricing page](https://www.runpod.io/pricing).

### Setup and Management

Bare metal setups give you full control but require more work up front, like hardware provisioning, OS installation, security hardening, and system tuning. While time-consuming, this process allows deep optimization for your specific training pipeline.

Traditional VMs are faster to launch and easier to manage. You can deploy pre-built images, scale quickly, and take advantage of built-in redundancy and auto-backups. If you need fast iteration and simplified ops, this lower-friction approach can accelerate your timeline.

## How to Choose Between Bare Metal Servers vs. Traditional VMs for Fine-Tuning

Choosing the right infrastructure depends on how you fine-tune models, whether you're running long, compute-heavy jobs or iterating quickly on experiments. Here’s how to decide between bare metal vs. traditional VMs based on your specific use case.

### When to Choose Bare Metal

Bare metal servers are ideal when you need maximum performance, reliability, and control.

Choose bare metal if:

* **Your fine-tuning jobs are long-running and predictable**: No virtualization overhead means better throughput and efficiency for sustained training runs.  
* **You're training large-scale models**: Direct hardware access ensures optimal GPU utilization—crucial for workloads with billions of parameters or high memory requirements.  
* **You require low-latency, high-throughput GPU access**: Bare metal avoids variability introduced by multi-tenant environments, keeping performance consistent.  
* **You're working with sensitive or regulated data**: Physical isolation reduces compliance risks tied to shared infrastructure.  
* **You need to customize hardware configurations**: Bare metal gives you full control over CPU, RAM, storage, and GPU pairings—ideal for specialized model architectures.

[PhoenixNAP](https://phoenixnap.com/blog/bare-metal-vs-vm) recommends bare metal for workloads that demand consistent, high-performance infrastructure—like deep learning and LLM training.

### When to Choose Traditional VMs

Virtual machines are better suited for flexible, dynamic workloads where fast setup and cost control matter most.

Go with traditional VMs if:

* **Your workloads are bursty or unpredictable**: VMs let you scale resources on demand—great for experimentation, A/B testing, or preprocessing.  
* **You're managing costs or avoiding large upfront investment**: VM pricing is pay-as-you-go, so you only pay for what you use.  
* **You need to spin up multiple environments quickly**: VMs are ideal for testing configurations across frameworks or pipelines.  
* **You benefit from rollback and snapshot features**: VMs often include built-in tools that simplify development and debugging workflows.

[Microsoft Azure](https://learn.microsoft.com/en-us/azure/baremetal-infrastructure/concepts-baremetal-infrastructure-overview) highlights traditional VMs as a strong fit for teams that need agility, automation, and operational simplicity.

## Why RunPod Is Ideal for AI Fine-Tuning on Bare Metal or Virtual Machines

Whether you need the raw performance of bare metal or the flexibility of VMs, RunPod delivers infrastructure built for modern AI fine-tuning. Its platform combines dedicated GPU power with cloud-like ease of use—allowing you to scale, optimize, and deploy faster.

### Simplified Deployment with High-Performance Hardware

RunPod bridges the gap between bare metal and traditional VMs by offering serverless GPU infrastructure that dynamically provisions compute based on actual usage. You get the performance benefits of dedicated hardware without the setup complexity or idle cost.

* [Bare metal GPU servers](https://www.runpod.io) give you full hardware control  
* On-demand access to cutting-edge [AMD GPUs](https://www.runpod.io/gpu/mi250) provides flexibility at scale  
* Prebuilt templates tailored for AI workloads accelerate setup  
* Tools like Axolotl make LLM fine-tuning fast and reproducible

Whether you’re building a custom model or fine-tuning open-source architectures, RunPod’s automated environment setup gets you training quickly—with no infrastructure guesswork.

### Operational Flexibility Without Virtualization Overhead

RunPod’s serverless model helps you avoid typical bottlenecks in fine-tuning environments:

* GPU and TPU resources are provisioned dynamically, so you don’t overpay for idle time  
* Serverless architecture allows you to scale up or down without manual intervention  
* Managed dataset handling and inference tools let your team focus on model development—not infrastructure

This balance of **bare metal performance** with **VM-like convenience** is ideal for teams that want speed without sacrificing control.

### Ready for the Future of AI Infrastructure

As AI infrastructure evolves, staying ahead means adapting to new hardware and distributed training models. RunPod makes this easier:

* Integrated access to TPUs, GPUs, and custom AI accelerators supports new training techniques  
* Support for distributed workflows prepares your team for trends like federated learning  
* Ongoing updates ensure your infrastructure stays optimized as AI models become more demanding

RunPod also tracks shifts in performance trade-offs as virtualized environments continue to improve. That’s why the platform gives you both options—letting you choose the right setup for your fine-tuning workload, whether it’s raw performance, flexibility, or a mix of both.

## Final Thoughts

The right infrastructure for fine-tuning AI models depends on your performance needs, workload patterns, and team priorities.

**Bare metal servers** deliver consistent high throughput and full hardware control—ideal for large-scale, resource-intensive training with predictable demands.

**Traditional virtual machines** offer greater flexibility and cost-efficiency, making them a strong choice for experimentation, dynamic workloads, or early-stage development.

Many teams adopt a hybrid approach: running core training jobs on bare metal while using VMs for preprocessing, dev/test, or overflow capacity. This gives you performance where it counts and flexibility where it matters.

When evaluating your setup, focus on workload predictability, GPU requirements, budget, and how much control you need over hardware. And if you want an infrastructure partner that can scale with your AI goals, RunPod gives you both: powerful bare metal when you need it, and elastic cloud flexibility when you don’t.

**Ready to fine-tune smarter? [Try RunPod’s bare metal GPU servers](https://www.runpod.io/gpu-bare-metal-server) and get started in minutes.**

Bare Metal vs. Traditional VMs for AI Fine-Tuning: What Should You Use?