Bare Metal vs. Virtualization: Which Performs Better for AI in 2025?

The rise of artificial intelligence has made infrastructure a strategic concern. Training foundational models, fine-tuning multilingual LLMs, or serving low-latency inferences isn’t decided solely by the prompt: it depends on how the work is executed, where the data resides, and what software/hardware layer mediates between the GPU and the framework. This raises a common question in architecture committees: bare metal or virtualization for AI? The short—yet honest—answer is it depends; the detailed discussion involves analyzing performance, efficiency, isolation, operation, and costs across different scenarios.

Below is a practical—no marketing or dogma—analysis to make an informed decision.

1) Performance: physics (almost always) favors bare metal

The critical path of current AI workloads is well known: GPU with high-bandwidth HBM, interconnection (NVLink/NVSwitch or PCIe), CPU for pre/post-processing, network (InfiniBand/Ethernet 100–400 Gb), and storage NVMe. Every additional layer between the code and the GPU introduces latency, copies, and less optimal cache content.

• Bare metal (without a hypervisor) provides direct hardware access. The OS scheduler and runtime (CUDA/ROCm, NCCL, cuDNN, Triton, etc.) see the topology as-is, allowing engineers to optimize NUMA placement, pinned memory, batch sizes, CPU–GPU affinity, and IO without “black boxes”.
• Virtualization adds a layer—KVM/QEMU, ESXi, Proxmox, Hyper-V, etc.—. With PCIe passthrough or SR-IOV, overhead can be low for many loads, but rarely zero. Using vGPU (GPU sharing), there’s a trade-off: higher utilization and multi-tenancy at the cost of variability and sometimes lower performance.

Operational conclusion:

• For distributed training (all-reduce, model/tensor/data parallelism) and intensive fine-tuning of LLMs, bare metal generally offers better performance and—most importantly—more stability.
• For inference, serving, and multiuser data science experiments, virtualization (or containers on hypervisors) can be sufficient and provide elasticity without critical penalties.

2) Where milliseconds are lost (or gained)

GPU–GPU interconnection. Jobs saturating NVLink/NVSwitch notice any deviations: less optimal topologies, shared queues, bus oversubscription. Bare metal allows fixing pod topology and ensuring precise affinity.

I/O and networking. All-reduce across InfiniBand NDR/HDR or Ethernet 200/400 Gb is latency-sensitive. Modern hypervisors support SR-IOV and DPDK for stack bypass, but require careful tuning and isolation to prevent cross-tenant interference.

Memory and NUMA. CPU–GPU affinity and pinning significantly impact preprocessing, feature stores, and data loaders. Virtualization can encapsulate NUMA decisions that, on bare metal, are explicit.

Scheduler. A queue scheduler (like Slurm, Kubernetes with Kueue/Volcano, Ray, etc.) can operate over bare metal or VMs. What’s critical is the lower layer and how maintenance windows, preemptions, and GPU sharing policies are scheduled.

3) How much performance is virtualization losing?

There’s no universal percentage: it depends on the load pattern (training vs. inference), passthrough or vGPU, batch size, I/O, network, and neighbor noise. In practice:

• With well-tuned passthrough (PCIe/SR-IOV), overhead can be small for many inferences and lightweight fine-tunes.
• Using vGPU or GPU sharing, performance may drop—or more precisely, vary—during training loads and latency-sensitive serving, though overall cluster utilization improves.

Golden rule: if the SLA is time-to-result (e.g., completing an epoch in X hours) or strict P95/P99 latency in milliseconds, choose bare metal. If the SLA is aggregate capacity (throughput at target cost) with some variability tolerance, virtualization can work.

4) Security, isolation, and compliance

• Bare metal offers physical isolation (dedicated machine), useful for regulated data (health, banking) and sensitive intellectual property. It also simplifies audits (fewer layers to review).
• Virtualization enables multi-tenancy with isolated network, storage, and compute; and policies like MIG (Multi-Instance GPU) by NVIDIA to partition GPUs at hardware level. Suitable for compliance, but requires controls and additional evidence.

Practical tip: if data sovereignty demands “dedicated machine and comprehensive traceability,” bare metal reduces friction. If the priority is securely sharing a common pool across teams, virtualization adds value.

5) Energy efficiency and density (the major elephant)

By 2025, pods with 60–80 kW per rack (and even >100 kW in pilots) will grow. Such density is not only about execution models, but bare metal helps maximize power utilization: fewer layers mean less loss and more predictable thermal behavior with liquid cooling (direct-to-chip or immersion). In virtualized setups, orchestrators must ensure co-location to avoid simultaneous peaks and thermal noise among tenants.

6) Operation: what’s easier?

• Bare metal simplifies data plane, but raises operational complexity: firmware, drivers, containers, images, power profiles, affinities… SRE for AI becomes critical (SLOs per job, preemptions, queues, leak detection, etc.).
• Virtualization enables multi-tenancy, live migration (limited with GPU), snapshots, and blast radius isolation, at the expense of an additional layer requiring maintenance (hypervisor patches, compatibility, tooling).

Tip: regardless of choice, adopt end-to-end observability (GPU, network, I/O, P95/P99 latency, €/kWh, €/model) and FinOps: assigning € to each epoch and each million tokens shifts discussions.

7) Costs: TCO beyond hourly price

• Bare metal often enhances time-to-result and cost per job when GPUs are the bottleneck, because it converts more watts into useful computation. Pay-as-you-go models are less flexible, but TCO per epoch can favor longer trainings.
• Virtualization excels in utilization: higher average resource usage, self-service for teams, and elasticity for spikes. The risk is paying “for convenience” with extra minutes/hours if overhead increases in your pattern.

Simple rule: measure €/result (€/epoch, €/10^6 tokens, €/P95 inferences) and not just €/hour. Assign energy cost (€/kWh) to the work—your first saving insight.

8) Decision matrix (summary)

9) Good practices to close the gap if you choose virtualization

1. PCIe passthrough / SR-IOV for GPU and NIC; avoid emulation.
2. Topology-aware scheduler configuration (Slurm/K8s + Volcano/Kueue): fix affinity GPU–CPU–NIC.
3. Low latency network (NDR/HDR or 200/400 GbE with RoCE), dedicated queues, and noisy neighbor isolation.
4. MIG (NVIDIA) when sharing offers higher utilization than the loss due to variability.
5. Storage: local scratch NVMe + burst buffers; avoid bottlenecks in preprocessing.
6. Tuning based on pattern: batch size, gradient accumulation, checkpointing, and quantization to reduce memory and I/O.

10) And Kubernetes?

Kubernetes is agnostic to the debate: it works on bare metal or inside VMs. The key is the physical layer (GPU, network, storage) and the operator (NVIDIA GPU Operator, RDMA, device plugins). For production AI, K8s offers multitenancy, lifecycle management, and GitOps; for massive training, many teams prefer Slurm or Ray over bare metal for fine-grained control over queues and networks.

Conclusion

• If your metric is time-to-result or extreme latency, and you perform distributed training or critical inference, bare metal currently offers the best performance and least variability.
• If your priority is utilization, self-service across teams, and elasticity for serving or data science, virtualization (with passthrough, SR-IOV, and/or MIG) can fit without relevant penalties, provided that you tune network, I/O, and scheduling.
• Whatever your choice, measure €/result and kWh per work unit, not just €/hour. AI in 2025 isn’t only about more GPUs, but about engineering infrastructure that turns watts into value with minimal noise.

Frequently Asked Questions

How much performance is lost by virtualizing a GPU for AI?
It depends on the pattern. With passthrough/SR-IOV and tuned networking, overhead can be low for many inferences; with vGPU/MIG, you gain utilization but might see variability and performance dips during intensive training. The only trustworthy figure comes from your benchmark (P95/P99, €/epoch).

Is MIG or vGPU worth it for inference?
Yes, when the load is granular and you prioritize utilization and multi-tenancy. For strict latency SLA or premium services, bare metal (or dedicated MIG partitions) are often better.

Kubernetes or Slurm for AI?
K8s is agnostic: it functions on bare metal or inside VMs. The key factors are the physical layer (GPU, network, storage) and the operator (NVIDIA GPU Operator, RDMA, device plugins). For production AI, K8s offers multitenancy, lifecycle management, and GitOps; for massive training, many prefer Slurm or Ray over bare metal for fine-grained control over queues and networks.

How do I compare costs between bare metal and virtualization?
Calculate €/result: €/epoch, €/10^6 tokens, or €/P95 inferences. Include energy costs (€/kWh), overhead degradation, operations, and risks (variability/SLA). This provides a more accurate view than just €/hour.