Migrating from AWS to Dedicated GPUs: A Performance and Cost Guide

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 our internal testing at Lyceum, bare-metal H100 clusters consistently show 10 to 15 percent 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 financial argument for migration has strengthened significantly over the last 12 months. According to a January 2026 report from Techstrong IT, AWS recently implemented a 15 percent price increase on its high-end GPU Capacity Blocks. For example, the hourly rate for a p5e.48xlarge instance (featuring H200 GPUs) has risen to approximately $39.80 in most regions, with some US West zones reaching nearly $50.00 per hour.

In contrast, dedicated GPU providers and specialized clouds offer H100 nodes for significantly less. Data from IntuitionLabs' November 2025 comparison shows that specialized providers often list H100s at $2.10 to $3.00 per GPU-hour. When you scale this to an 8-GPU node, the cost difference becomes staggering. A startup running a 30-day training job on a single 8-GPU node could save over $12,000 by choosing dedicated infrastructure over legacy cloud on-demand rates.

Furthermore, the NVIDIA B200 (Blackwell) transition is changing the ROI calculus. The B200 delivers up to 2.2 times the training performance of the H100, as noted in Uvation's 2025 benchmarks. However, legacy clouds often reserve their limited Blackwell capacity for their largest enterprise clients, leaving startups with older hardware at premium prices. Migrating to a sovereign provider like Lyceum ensures early access to B200 clusters with NVLink 5.0, which provides 1.8TB/s of bandwidth—double that of the previous generation.

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.

According to nOps' 2025 pricing analysis, these waivers typically require a support ticket and a planned 60-day exit window. This is a massive win for European deep-tech and biotech firms that want to move their workloads to sovereign infrastructure in Berlin or Zurich. 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 where your data never leaves European soil, and the underlying Protocol3 ensures that your orchestration layer is as secure as the hardware it runs on.

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 GPU Orchestration Tool provides a CLI and API that mimic the ease of the public cloud while running on bare-metal hardware.

Our orchestration layer is designed specifically for AI researchers. It handles the complexities of InfiniBand fabric, automates driver updates, and optimizes hardware selection to eliminate OOM errors before they happen. Instead of manually configuring a cluster, you can deploy a training job with a single command. This approach has been shown to double GPU utilization for many of our partners by ensuring that workloads are perfectly matched to the available VRAM and compute cycles.

1. Automated Provisioning

   Spin up H100 or B200 clusters in minutes, not weeks.

2. OOM Prevention

   Our scheduler analyzes your model's memory footprint and prevents deployment on insufficient hardware.
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.

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.