Question 1

What GPU do I need for LLM training?

Accepted Answer

For training large language models with 70B+ parameters, the NVIDIA H200 SXM5 is the right choice, it features HBM3e memory (141 GB), NVLink, and is optimised for multi-node training jobs. For smaller models (7B–13B) or inference workloads, the RTX 5090 with 32 GB GDDR7 delivers excellent performance at a lower price point.

Question 2

Can I run PyTorch and Hugging Face Transformers on your GPU servers?

Accepted Answer

Yes. Our GPU servers are bare metal, you have full root access and can install any version of PyTorch, CUDA, cuDNN, Transformers, vLLM, or any other ML library. There is no hypervisor overhead, no container restrictions, and no shared GPU time. What you benchmark is what you get in production.

Question 3

How do I connect multiple GPU nodes for distributed training?

Accepted Answer

Our GPU servers are connected via 10 Gbps or 25 Gbps Ethernet within the same datacenter. For distributed training with PyTorch DDP or DeepSpeed, you can use NCCL over Ethernet. We support any MPI implementation. For multi-node jobs requiring ultra-low latency, ask us about InfiniBand-capable configurations.

Question 4

What is the difference between the H200 and RTX 5090 configurations?

Accepted Answer

The H200 SXM5 is a data center GPU built for training and large-scale HPC: 141 GB HBM3e memory, NVLink 4, 4 TB/s memory bandwidth, and ECC support. The RTX 5090 is a workstation GPU optimised for inference, 3D rendering, and content creation: 32 GB GDDR7, 1792 GB/s bandwidth. Choose H200 for training 30B+ parameter models; choose RTX 5090 for inference serving, fine-tuning, or rendering.

Question 5

Do you support Jupyter notebooks and MLflow?

Accepted Answer

Yes. Because you have full bare metal access, you can install and run Jupyter Lab, MLflow, Weights & Biases, or any other experiment tracking and notebook tool. You can expose ports directly, set up reverse proxies, or access via SSH tunnel, there are no restrictions on software or ports.

Question 6

How long does it take to provision a GPU server?

Accepted Answer

Most dedicated GPU servers are provisioned within a few hours of order confirmation. GPU servers listed on our Server Auction page are pre-configured and can be online within minutes. Contact us for same-day provisioning availability.

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