GPU server setup help from our engineers

GPU server setup help is part of every OverGPU server. As part of our 24/7 support, our engineers help you check the server after handover and get your environment and data in place, at no extra fee; before you order, our calculators and guides help you size it. It is help and advice: you keep full root and full control, and we do not run your workloads for you.

What GPU server setup help includes

Onboarding covers three moments: before you order, when your server is delivered, and while you move your work in. Before you order, the help center, the docs, the GPU pages and the calculators answer sizing questions; once your server is delivered, an engineer helps through support tickets in your dashboard, at any hour.

Sizing before you order

Most of the cost is decided when you choose the GPU. Before you order, work out:

• the model, its parameter count and the precision you plan to use;
• whether you train, fine-tune or serve, and for serving, the context length and concurrency you expect;
• the size of your dataset and how long you need the capacity.

The VRAM calculator turns these into a memory estimate, and each GPU page lists what fits. A typical result: a 70B model needs about 70 GB in FP8, so one H100 is tight and one H200 is comfortable. In BF16 it needs about 140 GB, which makes an H200 tight and a B200 or B300 comfortable. Those figures cover the weights; add the KV cache and plan 10 to 30% headroom. Full fine-tuning with Adam needs roughly 16 to 20 bytes per parameter before activations, which turns a 70B model into a multi-GPU job. When you deploy, describe your workload in the notes for our engineers.

First-boot checks after handover

When your server is delivered and its IP address and SSH details appear in your dashboard, run a few checks before real work starts. We help you read the output.

nvidia-smi
nvidia-smi topo -m
docker run --rm --gpus all ubuntu nvidia-smi

The first confirms that every GPU is visible with its full memory. The second shows the interconnect, which on HGX servers means NVLink between all eight GPUs. The third confirms that containers can reach the GPUs.

On 8-GPU servers, an NCCL all-reduce test puts the interconnect under load. Build NVIDIA's nccl-tests and run, for example:

./build/all_reduce_perf -b 8 -e 8G -f 2 -g 8

If a GPU is missing or the bus bandwidth looks low, send us the output in a support ticket. Hardware faults are ours to fix: failed GPUs and components are replaced as a priority. On AMD MI355X servers, the equivalents are rocm-smi and RCCL tests. The multi-GPU and NCCL guide has more on these tests.

Environment and data migration advice

Open a support ticket about the layer between the image and your code:

• Drivers and CUDA. Which driver branch your framework expects, and whether a container's CUDA version suits the host driver.
• Containers. GPU containers with Docker and the NVIDIA Container Toolkit, or ROCm containers on AMD.
• Frameworks. Getting PyTorch, vLLM, SGLang or TensorRT-LLM to use every GPU, and the settings that matter for multi-GPU runs.

For data migration, we advise on moving datasets, checkpoints and model weights from another provider or from an earlier OverGPU server: rsync over SSH for directories, parallel streams for large transfers, and checksums before you delete the source. Inbound traffic is not counted, so uploading to your new server does not use your included bandwidth. If your current provider bills egress, plan for that cost on its side. Our PyTorch and vLLM guide and the OS images page cover the software side.

What GPU onboarding is not

Onboarding is help and advice, not managed operations. We do not:

• run, monitor or restart your training jobs or inference services;
• maintain your application code, models or containers;
• back up your data, unless agreed in writing;
• access your data, except to operate or repair the service at your request, or when the law requires it.

What we do own is the infrastructure: power, network and hardware, under a 99.9% monthly availability SLA with service credits, and the 24/7 support behind it. If a GPU fails, that is ours to fix. Your workloads stay yours to run.

How to get setup help

Before you order, answers are in the help center, the docs, the GPU pages and the calculators, and when you order, the notes for our engineers can describe your workload; once your server is delivered, open a support ticket from your dashboard, and an engineer answers at any hour. Include your server, what you are trying to do and any command output. There is no separate fee and nothing to book. Our page on getting support explains what to include in a request.