How to Migrate from Cloud GPU to Dedicated GPU Hosting

Why Migrate from Cloud to Dedicated GPU Hosting?

Teams that started on cloud GPU platforms often reach a point where the economics no longer make sense. Per-hour billing that seemed reasonable during prototyping becomes expensive at scale, and the unpredictable costs of egress fees, storage charges, and spot instance interruptions create budgeting headaches. Moving to dedicated GPU hosting with fixed monthly pricing provides cost predictability, better performance through bare-metal access, and full control over your infrastructure.

The dedicated GPU vs cloud GPU comparison highlights the specific cost crossover points. For workloads running more than a few hours per day, dedicated hosting typically delivers 40-70% savings over cloud GPU providers. Beyond cost, teams gain consistent performance without noisy-neighbour effects, data residency guarantees in UK datacentres, and the ability to customise every layer of the stack.

Pre-Migration Audit: Assess Your Current Setup

Before migrating, document your current cloud GPU environment thoroughly. This audit prevents surprises during the transition and ensures your dedicated server matches or exceeds your current capabilities.

Export a full list of installed packages using pip freeze or conda list --export. Record your Docker images if containerised. This documentation becomes your migration checklist. For cloud providers that offer alternatives, review the RunPod alternatives guide to understand your options.

Choosing Equivalent (or Better) Hardware

Map your cloud GPU instance to an equivalent dedicated server configuration. In many cases, you can achieve better performance for less cost because bare-metal servers eliminate virtualisation overhead, giving you the full performance of the hardware.

Use the GPU server selection guide to match your workload requirements to specific hardware. The GPU comparisons tool helps evaluate specific cards side by side. For LLM inference workloads, the best GPU for LLM inference analysis provides model-specific recommendations.

Setting Up Your Dedicated Server Environment

Once your dedicated server is provisioned, set up an environment that mirrors your cloud configuration. With full root access on bare-metal hardware, you have complete freedom over the software stack.

Start with the operating system and NVIDIA drivers. GigaGPU servers come with Ubuntu pre-installed and NVIDIA drivers configured. Verify the CUDA version matches your framework requirements, then install your ML frameworks. For inference deployments, follow the vLLM production setup guide or the self-hosting LLM guide for step-by-step instructions.

Containerisation with Docker simplifies this process. If your cloud workload runs in a Docker container, that same container runs on bare metal with minimal changes. Simply install Docker and the NVIDIA Container Toolkit, then pull your existing images. The key advantage is that your container now has direct GPU access without the cloud hypervisor layer.

Transferring Data and Model Weights

Data transfer is often the most time-consuming part of the migration. Plan this step carefully to minimise downtime and ensure data integrity.

For model weights, download directly from Hugging Face or your model registry to the new server rather than transferring from the cloud instance. This is often faster and avoids cloud egress charges. For custom fine-tuned models, use rsync or scp over SSH for secure, resumable transfers.

Testing and Validation

Before cutting over production traffic, validate that your dedicated server produces identical results to your cloud environment. Run your test suite with known inputs and compare outputs byte-for-byte where possible.

Key validation steps include verifying model output consistency by running identical prompts through both environments, load testing with your expected peak traffic using tools like locust or wrk, monitoring GPU utilisation, VRAM usage, and temperatures under load, and verifying that your monitoring and alerting systems receive metrics from the new server.

For inference workloads, compare tokens per second, P50 and P99 latency, and throughput under concurrent load. Use the tokens per second benchmark as a baseline reference. Bare-metal performance should meet or exceed your cloud benchmarks due to the elimination of virtualisation overhead.

Production Cutover Strategy

Choose a cutover strategy that matches your uptime requirements. For non-critical workloads, a simple DNS switch during a maintenance window is sufficient. For production services with strict availability requirements, implement a gradual migration.

The recommended approach for most teams is load-balancer-based weighted routing. Start by sending 10% of traffic to the dedicated server, monitor for errors and performance degradation, then gradually increase to 100%. Keep the cloud environment running for 48-72 hours after full cutover as a rollback option, then decommission it.

After migration, you will benefit from GigaGPU’s 99.9% uptime SLA and fixed monthly pricing with no surprise charges. For teams running at scale, the scaling AI inference to production guide covers how to grow your dedicated infrastructure as demand increases. Explore available configurations in the tutorials section for more deployment guides.