The Virtualization Tax: Why Virtualized GPUs Underperform

When you rent a p5.48xlarge instance on AWS, you are not just paying for eight NVIDIA H100 GPUs. You are paying for the massive software stack required to make those GPUs work in a multi-tenant environment. This 'virtualization tax' manifests as hypervisor overhead, which can introduce micro-latencies in GPU-to-GPU communication. For distributed training workloads that rely on NCCL (NVIDIA Collective Communications Library), even minor latencies in the interconnect can lead to significant synchronization bottlenecks.

RDMA and Bare-Metal Networking Advantages

Dedicated GPUs provide raw, bare-metal access. This means your containers interact directly with the hardware without an intermediate layer like the Nitro System. In practice, bare-metal H100 clusters show consistently higher throughput in large-scale training tasks compared to their virtualized counterparts. This performance gap is even more pronounced when using InfiniBand or RoCE (RDMA over Converged Ethernet), where direct hardware access is critical for low-latency data transfers.

  • Noisy Neighbors: In a public cloud, other tenants on the same physical host can impact your I/O performance.
  • Opaque Orchestration: Legacy clouds often use generic schedulers that do not understand the specific memory requirements of a 175B parameter model.
  • Fixed Configurations: You are often forced into rigid instance sizes that result in underutilized CPUs or RAM just to get the GPU count you need.

By moving to a dedicated environment, you can customize the host-to-GPU ratio. If your workload is memory-bound rather than compute-bound, you can pair H100s with higher-capacity local NVMe storage or specific amounts of system RAM, ensuring that no part of your infrastructure sits idle while the meter is running.

The 2026 Economic Reality: Comparing Costs

The financial argument for migration rests on rates anyone can check. The AWS EC2 Capacity Blocks for ML pricing page, read on 3 August 2026, lists the p5e.48xlarge instance (eight H200 GPUs) at an effective $47.76 per instance-hour in US East (Ohio), which the same table normalizes to $5.97 per accelerator-hour. The p5.48xlarge (eight H100 GPUs) lists at $41.528 per instance-hour, or $5.191 per accelerator-hour.

In contrast, dedicated GPU providers and specialized clouds price H100 nodes below that, though the size of the gap moves from month to month. Compare on the day you buy, and read the per-accelerator column on both sides: setting an eight-GPU node price against a single-GPU rate overstates the difference by a factor of eight. Lyceum bills GPU compute per second with no base fee, which changes the arithmetic again for jobs that do not run a full hour.

Furthermore, the NVIDIA B200 (Blackwell) transition is changing the ROI calculus. NVIDIA's DGX B200 product page states 3 times the training performance and 15 times the inference performance of the previous-generation system. However, legacy clouds often reserve their limited Blackwell capacity for their largest enterprise clients, leaving startups with older hardware at premium prices. Blackwell systems pair that with fifth-generation NVLink, which NVIDIA specifies at 1,800 GB/s per GPU, twice the 900 GB/s of the fourth generation.

Leveraging the EU Data Act and Egress Waivers

One of the biggest historical barriers to migration was the cost of moving data. For AI companies with petabyte-scale datasets, egress fees were effectively a 'ransom' for their own data. However, the regulatory landscape changed in 2024 and 2025. In response to the European Data Act, major cloud providers, including AWS, began waiving egress fees for customers who are permanently migrating their data off the platform.

In AWS's announcement of 5 March 2024, the waiver is requested through AWS Support and eligible customers have 90 days to complete the move off AWS. This is a massive win for European deep-tech and biotech firms that want to move their workloads to sovereign European infrastructure in Spain, Paris or the Nordics. By utilizing these credits, you can transfer your entire training corpus to a dedicated environment without the six-figure networking bill that used to accompany such a move.

Sovereignty is not just about cost; it is about compliance and security. For companies handling sensitive medical data or proprietary research, the 'black box' nature of US-based hyperscalers is a liability. Lyceum Technology provides a sovereign alternative that runs in European data centers in Spain, Paris and the Nordics, with the orchestration layer engineered to the same security standard as the hardware it runs on.

Orchestration without the DevOps Overhead

The primary reason teams stay on AWS is not the hardware; it is the ecosystem. The fear is that moving to dedicated GPUs requires hiring a 10-person DevOps team to manage Kubernetes clusters, drivers, and networking. This is where modern orchestration layers change the game. Lyceum's orchestration tooling provides a CLI and API that mimic the ease of the public cloud, on GPU VMs with full root and SSH access.

Our orchestration layer is designed specifically for AI researchers. It handles the complexities of InfiniBand fabric, automates driver updates, and selects hardware with a scheduler that predicts memory use and runtime within a node, which helps prevent OOM errors before they happen. Instead of manually configuring a cluster, you can deploy a training job with a single command. This approach keeps GPU utilization high by ensuring that workloads are matched to the available VRAM and compute cycles.

  1. Provisioning: Provision GPU VMs on demand. Reserved cluster capacity is planned with the account team: two to three weeks notice to add or remove, around four weeks lead time for new machines.
  2. OOM Prevention: Our scheduler predicts memory use and runtime within a node and places the job on the GPU that fits. Prediction across more than four GPUs per node is in development.
  3. Sovereign Control: Manage your infrastructure through a unified interface that prioritizes technical clarity over corporate jargon.

By abstracting the 'ops' but not the 'hardware,' we allow AI engineers to focus on their models while enjoying the performance benefits of dedicated silicon.

The Technical Migration Roadmap

A successful migration from AWS to dedicated GPUs follows a structured technical path. The goal is to minimize downtime and ensure that your training state is preserved. Most teams start by containerizing their entire environment using Docker or Apptainer (formerly Singularity). This ensures that your CUDA versions, libraries, and dependencies are portable across different hardware environments.

Next, address the data layer. While the egress fees might be waived, the physical transfer of petabytes still takes time. We recommend a phased approach: sync your primary dataset to the new dedicated storage using rclone or AWS DataSync, then use a final 'delta sync' right before the cutover. For real-time workloads, a hybrid approach can work where inference remains on the cloud while heavy training moves to dedicated nodes, though this can introduce latency if not managed correctly.

Finally, verify your networking. Dedicated clusters often use RDMA for high-speed communication. You will need to ensure your training scripts are configured to use the correct network interface. Lyceum's CLI simplifies this by automatically detecting the optimal networking path for your cluster, ensuring that your AllReduce operations are running at peak efficiency from day one.

See what dedicated GPU alternatives cost. Use the GPU Pricing Calculator to compare costs across RunPod, Lambda, AWS, GCP, CoreWeave, and Lyceum.

Sources

[1] Amazon Web Services, EC2 Capacity Blocks for ML pricing (read 3 August 2026); [2] Amazon Web Services, Free data transfer out to internet when moving out of AWS, 5 March 2024 (read 3 August 2026); [3] NVIDIA DGX B200 product page (read 3 August 2026); [4] NVIDIA, NVLink and NVLink Switch specifications (read 3 August 2026)