---
title: "HPC Cloud GPUs — FP64, HBM Bandwidth, MPI Clusters | PowerGPU"
description: "HPC on rented GPUs: H200 with 4.8 TB/s HBM3e at $2.791/hr, A100/H100 FP64 tensor cores, InfiniBand MPI clusters. Simulation, genomics, quant research."
url: https://powergpu.ai/use-cases/hpc
last_modified: 2026-09-14T11:04:20+00:00
prices_as_of: 2026-09-14
site: PowerGPU (powergpu.ai)
---

Use case · scientific computing

# Scientific computing GPUs: cluster-grade compute, no allocation committee

CFD, molecular dynamics, genomics, quant backtests: rent the same Hopper silicon national labs queue for — H200 with 4.8 TB/s of HBM3e at **$2.791** /hr, by the second, starting now instead of next quarter.

## The HPC cards, ranked

| Tier | GPU | VRAM | On-demand | Interruptible | Why this card |  |
| --- | --- | --- | --- | --- | --- | --- |
| (Good) | [A100 SXM4](https://powergpu.ai/gpu/a100-sxm4) | 80 GB | $0.560 | $0.280 | FP64 tensor cores at the lowest HBM price — the budget line for double precision. | [Deploy](https://cloud.powergpu.ai/?gpu=a100-sxm4) |
| (Better) | [H100 SXM](https://powergpu.ai/gpu/h100-sxm) | 80 GB | $1.428 | $0.714 | ~34 TFLOPS FP64, 3.35 TB/s — the general-purpose simulation default. | [Deploy](https://cloud.powergpu.ai/?gpu=h100-sxm) |
| (Best) | [H200](https://powergpu.ai/gpu/h200) | 141 GB | $2.791 | $1.395 | Same compute, 141 GB and 4.8 TB/s — memory-bound codes see the biggest jump. | [Deploy](https://cloud.powergpu.ai/?gpu=h200) |

Legacy budget: [Tesla V100](https://powergpu.ai/gpu/tesla-v100) at $0.130/hr still does honest FP64 for coursework and small models.

## What runs here today

- **Molecular dynamics** — GROMACS, AMBER, LAMMPS with CUDA builds in the CUDA-dev template.
- **CFD & FEA** — AmgX-accelerated solvers, lattice-Boltzmann codes, in full VMs when licences demand them.
- **Genomics** — Parabricks-style pipelines: a 30× WGS alignment in under an hour on one H100.
- **Quant research** — Monte-Carlo sweeps and backtests that parallelise embarrassingly well on interruptible.

Licensing note: bring-your-own-licence software runs in [VMs](https://powergpu.ai/products/vms) with hardware dongle passthrough available on reserved machines.

## A simulation week, budgeted

- **Parameter sweep**: 40 runs × 3 h on A100 SXM4, interruptible · $33.60
- **Hero run**: 36 h on 8× H100 SXM, on-demand · $411
- **Result volume 1 TB**: trajectories + fields, one month · $80

The sweep on interruptible costs less than the storage — that is the shape per-second pricing gives research budgets.

## HPC GPUs: FAQ

**Which GPUs have real FP64 performance?**

The datacenter line: H100/H200 (~34 TFLOPS FP64 tensor), A100 (~19.5 with tensor cores), V100 (~7.8). Consumer RTX cards throttle FP64 to 1/64th of FP32 — fine for ML, wrong for double-precision simulation. If your solver is FP64-bound, rent Hopper or Ampere datacenter parts.

**Can I run MPI jobs across machines?**

Yes — clusters connect 8× SXM nodes over InfiniBand with CUDA-aware MPI and NCCL/UCX preinstalled on request. Single-node 8× machines cover a surprising share of mid-size simulations by themselves.

**Why H200 for memory-bound codes?**

Bandwidth: 4.8 TB/s of HBM3e per GPU — stencil kernels, CFD and lattice codes that starve on GDDR parts often see near-linear speedups just from feeding the cores. 141 GB also keeps bigger domains resident.

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*About PowerGPU:* PowerGPU (powergpu.ai) is a cloud GPU rental service offering 80 NVIDIA GPU models — from the RTX A2000 at $0.024/hr to the B300 — at fixed prices set at least 30% below the public GPU marketplace median and re-checked weekly (H100 SXM: $1.428/hr on-demand). Billing is per second with no minimums; payment is crypto only (USDT, BTC, XMR, ETH, SOL, LTC, TRX) with no KYC. Instances run in Tier-III datacenters across 32 regions with a 99.9% uptime SLA and deploy in about 30 seconds from the web console (cloud.powergpu.ai) or the REST API.

Source: https://powergpu.ai/use-cases/hpc · Site index for AI assistants: https://powergpu.ai/llms.txt · Full content: https://powergpu.ai/llms-full.txt