GPU servers for research and HPC, on fixed monthly budgets

GPU servers for research and HPC have to match the numerics of your code before anything else. A molecular dynamics package that runs in mixed precision, a CFD solver that needs FP64 throughout and a protein model that runs in BF16 want different hardware. We rent NVIDIA H100 and H200 servers with CUDA and AMD Instinct MI355X servers with ROCm by the month, so a project gets dedicated GPUs at a price fixed before it starts.

FP64: where it matters and which GPU to pick

Double precision matters for codes where rounding errors accumulate: electronic-structure and quantum chemistry packages, many CFD and finite-element solvers, and some climate and astrophysics models. Many molecular dynamics engines, such as GROMACS and AMBER, run their GPU paths in mixed precision, and AI-for-science models run in BF16 or FP8. For those, FP64 throughput is not the deciding figure.

NVIDIA lists the H100 and H200 figures, AMD the MI355X figure. FP64 Tensor Core throughput applies to matrix operations, such as DGEMM-heavy dense linear algebra through cuBLAS; the vector figure applies to everything else. The RTX PRO 6000, RTX 5090 and L40S are designed for graphics, AI and single-precision work, so we do not recommend them for FP64-bound codes. For Blackwell data-center GPUs, check NVIDIA's current FP64 specifications for your exact part before choosing one for double-precision work.

Choosing GPU servers for research and HPC

Many HPC codes, including stencils, sparse solvers and lattice methods, are limited by memory bandwidth rather than peak FLOPS, so the 4.8 TB/s of the H200 or the 8 TB/s of the MI355X can matter more than any compute figure. Memory per GPU sets the largest domain you can hold without decomposing it further. A double-precision 3D grid of 1,000 × 1,000 × 1,000 cells with 10 variables per cell takes about 80 GB: that fills an H100 but fits on an H200 or an MI355X with room for solver workspace.

• One H100: port, profile and validate a code, or run single-GPU jobs.
• HGX H100 or HGX H200: eight GPUs on NVLink at 900 GB/s per GPU for halo exchanges and collective operations, eight 400G ConnectX-7 adapters for multi-node runs, and 2 TB of DDR5.
• 8× MI355X: 2.3 TB of HBM3e in one node, 3 TB of DDR5 and eight 400G RoCE adapters, for large memory-bound problems on ROCm. See the AMD Instinct MI355X page.

For MPI across nodes, ask for private networking between your servers in the order notes.

Profile before you scale

Before booking an 8-GPU node for months, profile on one GPU. NVIDIA Nsight Systems and Nsight Compute, or rocprof on AMD, show whether a kernel is bound by memory bandwidth or by compute, which tells you whether the H200's extra bandwidth or the MI355X's FP64 figure will pay off. Then measure strong scaling from one to two, four and eight GPUs: if efficiency drops sharply past four, run two four-GPU jobs side by side on the node, or rent single GPUs instead. Plan storage the same way. Checkpoint-restart files and output fields add up fast; the HGX and MI355X nodes each carry eight 3.84 TB NVMe drives, about 30 TB raw, for scratch space, with 50 TB of outbound traffic a month to bring results home.

CUDA, ROCm and your software stack

NVIDIA servers start from Ubuntu 22.04 or 24.04 with drivers, CUDA, Docker and the NVIDIA Container Toolkit. Add the NVIDIA HPC SDK, a GPU-aware MPI such as Open MPI with UCX, or your group's containers. If your home cluster uses Apptainer, install it so the same images run in both places. Setup notes are in our NVIDIA drivers and CUDA guide.

AMD servers use ROCm images. CUDA code needs a HIP port, which the hipify tools help automate, or a portable backend such as Kokkos, SYCL or OpenMP offload. PyTorch and JAX have ROCm builds. Check that your code and its version support ROCm before choosing the MI355X, and start from our ROCm guide. Debian or Rocky Linux images are available on request.

Fixed monthly budgets for research projects

Each server has one flat price, billed monthly in advance and fixed for each paid month, and we give at least 30 days' notice before any price change for future months. A three-month simulation campaign on an HGX H100 node costs 3 × $16,900 = $50,700, with 50 TB of outbound traffic per month included and inbound traffic not counted. A single H100 is $2,190 a month, and the 8× MI355X node is $19,900.

A dedicated node removes queue waits when shared clusters fill up before a deadline, and gives you root access for custom stacks. For short bursts of a few days a month, hourly capacity elsewhere will cost less. Once you have an active server, you can ask by ticket for term pricing for 6–12 months or several servers, usually from three. Labs that work with grants and procurement offices should also read our page on GPU servers for universities and research labs.

Getting started

Deploy a server from one form: choose the configuration, set up the OS image, optional preinstalled software and your login (root password or SSH key), create a free account or log in, and pay the first month from your prepaid balance, topped up in crypto. Your code, precision, problem size and node count can go in the optional notes for our engineers. The server is delivered in under 10 minutes after payment, with the IP and SSH details in your dashboard. Our engineers provide 24/7 support for incidents through tickets in your dashboard.