Your GPU isn't the bottleneck.
Your CPU is.
Most teams assume virtualization tanks GPU performance. Benchmarks say otherwise. GPU passthrough on KVM hits 98-100% of native speed. The GPU barely notices the hypervisor.
But here's what actually gets hit: your CPU.
A 2025 study measured 13% average end-to-end training overhead in virtualized environments, rising to 37% on preprocessing-heavy 8-GPU jobs. Data loading. Tokenization. Image decoding. All running on shared CPU cores.
The faster your GPUs, the harder they expose a slow input pipeline.
So the real question isn't "bare metal or virtual?"
It's: is your workload GPU-bound or pipeline-bound?
GPU-bound with a light pipeline? Virtualization costs you 2-4%. The 57% bare metal premium is hard to justify.
CPU-saturated, feeding fast GPUs? Bare metal's full-node allocation can recover 10-30% of end-to-end throughput.
Two more cases where bare metal wins, no debate:
Compliance workloads (HIPAA, PCI DSS, gov). Auditors love "nothing else runs on this machine." Production inference APIs where p99 latency is contractual.
Everything else? Bursty dev, sweeps, checkpointed training. Virtualized cloud almost always wins. Pay for what you use, scale in minutes, no fixed node commitment.
On packet.ai, bare metal B200 runs $5.90/GPU-hr vs. $3.75/GPU-hr on virtualized Dynamic PODs. That's roughly $12,600/month more for an 8-GPU node. Worth it above 70% sustained utilization. A money pit below it.
*Full cost math and decision framework in the packet.ai blog - *
[https://packet.ai/blog/bare-metal-gpu-server-vs-virtualized-gpu-cloud]
*If you are searching for B200 at best price for your workload checkout below - *
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