From AI experiments to production workloads. Find your next GPU with Xanpath. Explore GPUs.
Hopper architecture and high-bandwidth memory for compute-intensive AI projects.
80 GB of GPU memory to consider for your model, dataset or creative workflow.
Start with the application. Include GPU count, supporting resources and the environment your team needs.
Discuss availability and pricing with Xanpath. Shape a proposal around your project.
Hopper architecture and high-bandwidth memory for compute-intensive AI projects.
GPU memory is a starting point, not the whole decision. Consider precision, model size, interconnects and the software stack you plan to use.
Server resources, availability and supported features depend on the proposed configuration.
NVIDIA product informationHBM3 GPU memory
NVIDIA architecture
Memory bandwidth
Evaluate with your software and data
Prepare a complete brief so that compute, storage and networking work together.
| Resource | Discuss with Xanpath |
|---|---|
| GPU allocation | Your required GPU count and whether the application can use multiple GPUs |
| CPU & system memory | Data preparation, loading and the memory needed outside the GPU |
| Storage | Dataset, checkpoint and output capacity; persistence and backup |
| Networking | Preferred region, latency, bandwidth and traffic volume |
| Project duration | Configuration | Pricing |
|---|---|---|
| Short evaluation | Scope an initial workload and test plan | Request a quote |
| Production project | Discuss capacity, support and commercial terms | Request a quote |
| Longer commitment | Review the duration and resource requirements | Request a proposal |
Share your framework and required versions, along with your operating system and driver constraints.
Include reproducibility, containers and notebook access in your requirements so they can be reviewed together.
Prepare your environmentStart with the workload. Shape the infrastructure around it.
Training data, checkpoints and rendered output all need a storage plan.
Discuss persistence, transfers and recovery responsibilities before choosing a compute configuration.
Plan your integrations# A starting point for your compute brief
workload: language-model-inference
accelerator: nvidia-h100
memory: discuss-with-xanpath
environment: pytorch
region: your-preferred-location
next_step: request-a-proposalNotebooks and reproducible environments can make model development easier to organize.
Include Jupyter, Python packages and framework versions in your requirements. Discuss how the environment will be prepared and accessed for your project.
Prepare your software checklistimport torch
# Check the environment for your workload
device = "cuda" if torch.cuda.is_available() else "cpu"
print(device)cuda# Your next experiment starts here
# Prepare data, load the model, evaluate.A GPU is one part of your infrastructure. Power, networking, access control and maintenance all deserve the same attention.
Discuss your availability targets, backup responsibilities and support needs with us. The agreed service conditions belong in your proposal.
Security & reliability
Good infrastructure starts with understanding what you want to build. Xanpath brings the conversation back to your workload, your constraints and your next step.
From GPU selection to the wider compute environment, let’s put the details into a practical plan.
Tell us your preferred region and data requirements. Location and availability are confirmed with your proposal.
A quote depends on GPU count, supporting resources and project duration. Request H100 SXM pricing.
Check the application’s GPU, memory and software requirements. Share the relevant versions and a sample workload when requesting a configuration.
Use your own model or scene, specify precision and batch size, and record the full environment. Hardware specifications alone do not predict end-to-end speed.
Bring your workload. We’ll start with the questions that matter.