AI teams and ML engineers need infrastructure that’s flexible, powerful, and cost-effective. Bare Metal GPUs—dedicated physical GPU servers without virtualization—have become a go-to solution for meeting these demands. Bare Metal GPUs have emerged as a compelling solution to meet these needs.

Unlike standard cloud GPU instances (which run on virtual machines) or on-premises clusters (which require heavy upfront investment), bare metal GPU servers offer direct hardware access and consistent, high-end performance without the “hypervisor tax” or multi-tenant interference.

In this post, we’ll dive deep into the technical advantages of bare metal GPUs, compare them with virtualized cloud GPUs and on-prem solutions, share industry insights on why many organizations are shifting to bare metal, address common pain points (like latency and unpredictable costs), and highlight real-world examples – including how AI teams are benefitting from Runpod’s new Bare Metal offering.

What Are Bare Metal GPUs? The Definition:

A Bare Metal GPU refers to a physical Graphics Processing Unit (GPU) provided directly to a single user or tenant without virtualization or shared resources. It offers dedicated, direct access to the GPU hardware, enabling full utilization of its computational capabilities, higher performance, reduced latency, and better predictability.

Bare Metal GPUs are particularly beneficial for resource-intensive tasks such as deep learning, AI workloads, graphics rendering, scientific simulations, and high-performance computing (HPC), where virtualization overhead needs to be minimized and consistent, powerful GPU performance is required.

With Bare Metal, you can now reserve dedicated GPU servers for months or even years, ensuring consistent performance without the hassle of hourly or daily pricing.

This means significant cost savings and a machine that’s entirely yours, with no virtualization layer getting in the way.

Supercharge your AI efforts with some serious GPU muscle! NVIDIA’s H100 bare metal GPUs from Runpod are ready to help you train beefier models, crunch inference at warp speed, and boldly go where no AI has gone before. Fill out this quick Bare Metal form and find out how Runpod’s Bare Metal H100s can take your AI projects from “meh” to mind-blowing!

This means significant cost savings and a machine that’s entirely yours, with no virtualization layer getting in the way.

Technical Deep Dive into Bare Metal GPU Performance

When it comes to raw performance, removing virtualization makes a significant difference.

Even a well-optimized VM introduces overhead – often on the order of 5–10% of CPU/GPU resources – just to run the hypervisor​.

In practice, “bare metal” GPU instances tend to outperform virtualized ones across CPU, memory, and IO benchmarks. For example, a Kubernetes study found bare metal nodes achieved over 100% higher throughput (i.e. more than double the performance) compared to VMs in tests of CPU, RAM, disk, and network – a staggering difference​.

![][image1]

While that is an extreme case, it underscores that the combination of hypervisor overhead and “noisy neighbors” (other tenants contending for shared resources) can degrade performance. In cloud VM environments, a busy neighboring VM can cause an additional 20–30% performance loss due to resource contention . Bare metal GPUs avoid these pitfalls entirely: with a dedicated GPU server, there’s no hypervisor tax and no noisy neighbors siphoning.

The result is performance that is on par with or better than on-premises dedicated servers. In fact, NVIDIA reports that on their latest hardware (e.g. the H100), virtualization overhead can be roughly 4–5% for GPU-heavy workloads​.

That small overhead might be tolerable for light inference jobs, but for large-scale training pushing a GPU to its limits, even a 5% slowdown is non-trivial.

Bare metal ensures 100% of the GPU’s horsepower is directed at your workload.

This is why Runpod is excited to introduce Runpod Bare Metal as a part of our core offering. It’s providing teams with direct access to the underlying server, the ability to run any software stack, install custom drivers, and configure the machine however they need.

On-premises GPU clusters, which are typically bare metal by nature, offer similarly unfettered performance – assuming you’ve invested in the latest hardware.

The difference is, on-prem capacity is fixed by what you bought. By contrast, bare metal cloud providers can offer cutting-edge GPUs on demand (like NVIDIA A100s or H100s) without you having to procure new machines. In short, bare metal cloud GPUs deliver the speed of on-prem, with the flexibility of cloud.

GPU Architecture and Why It Matters for AI/ML

![][image2]
SOURCE: Hot Chips, NVIDIA Chief Scientist Bill Dally

To appreciate why bare metal access to GPUs is so powerful, it helps to understand GPU architecture. Modern GPUs are parallel-processing beasts: they contain thousands of small cores that can work simultaneously on computations, whereas a typical CPU has only a handful of powerful cores​. AI models – essentially giant stacks of linear algebra operations – map exceptionally well onto this parallel architecture.

GPUs can “slice through the math” of neural networks by performing many multiplications and additions in parallel​ for example, NVIDIA’s A100 GPU packs over 6,900 CUDA cores and specialized Tensor Cores (matrix-multiply units) that deliver up to 312 TFLOPS of FP16 compute – orders of magnitude beyond a CPU’s throughput.

These Tensor Cores are purpose-built to accelerate AI computations (like multiplying large matrices) and according to NVIDIA are 60× more powerful for deep learning ops than first-gen GPU designs​ . In practical terms, this means complex model training that would take weeks on CPUs can finish in hours on a cluster of GPUs.

Bare metal gives you unfettered access to all of these hardware capabilities. There’s no virtualization layer limiting features or splitting up the GPU. You can leverage high-bandwidth GPU memory fully, utilize NVLink interconnects between GPUs (for multi-GPU servers), and push the hardware to its peak. GPU memory bandwidth is crucial for big-data workloads – high-end GPUs use HBM2e or HBM3 memory offering TB/s of throughput, feeding those cores with data at extreme speeds.

Additionally, many bare metal GPU servers (like NVIDIA’s HGX platforms) feature NVLink or NVSwitch connections that allow GPUs to communicate with each other at 600+ GB/s, far faster than traditional PCIe. This architecture is a boon for large-scale training that spans multiple GPUs, as it keeps the GPUs fed with data and synchronized with minimal delay. In essence, GPUs accelerate AI/ML by combining massive parallelism with specialized hardware (tensor cores, high-speed memory), and a bare metal environment lets you harness 100% of that acceleration. As one NVIDIA engineer succinctly said, *“GPUs have become the foundation of artificial intelligence”*​ – they are the engines driving today’s AI breakthroughs.

Workload Optimization on Bare Metal GPUs

Having raw horsepower is great, but maximizing GPU efficiency requires smart optimization.

Bare metal access can actually help in this regard, because you have full control of the software stack and hardware settings. Here are a few techniques and benefits for optimizing AI/ML workloads on bare metal GPU servers:

Custom Software Stack & Drivers

Bare metal means you can install any drivers, libraries, or OS tweaks you need. You’re not limited to a cloud provider’s VM image.

This enables using the latest NVIDIA CUDA toolkit, specialized libraries, or even custom kernel modules to squeeze out extra performance. For instance, if a new version of CUDA or cuDNN offers 10% faster training throughput, you can deploy it immediately on your dedicated server. Full hardware control also means you can enable features like GPU peer-to-peer communication, NUMA affinity tuning, or CPU pinning to optimize how data flows to the GPU​.

Parallel Data Pipelines

To keep a powerful GPU busy, you need to feed it data without bottlenecks. On a bare metal server, you can utilize high-speed NVMe storage and tune your I/O subsystem for fast data loading.

Techniques like overlapping computation and data pre-fetch (using separate CPU threads to load/augment data while the GPU crunches numbers) can drive GPU utilization close to 100%. Because you’re the sole tenant, you can fully saturate disk or network bandwidth without worrying about throttling from a host hypervisor. This ensures the GPU isn’t starved for data.

Mixed Precision and Tensor Cores

Bare metal GPUs allow you to use mixed-precision training (FP16 or BF16) freely, taking full advantage of tensor cores for 8x faster matrix operations​. While you could do this in a VM too, bare metal gives more consistent latency which helps when using aggressive mixed-precision and dynamic loss scaling.

Many ML teams have found that using FP16 precision on NVIDIA A100/H100 GPUs (with code frameworks like PyTorch or TensorFlow) dramatically speeds up training with minimal accuracy loss – effectively getting more done per GPU-hour.

Scaling Out with Multi-GPU and Multi-Node

If one GPU isn’t enough, bare metal lets you connect multiple GPUs with optimal throughput. For example, Runpod’s bare metal servers come in configurations of 8× A100 or H100 GPU...