NVIDIA GPU servers for demanding workloads.
Purpose-built GPU infrastructure for training, inference and discovery.
Compute for your next breakthrough.
Plan GPU capacity around your model, framework and data pipeline. Consider accelerator memory alongside CPU performance and storage throughput.
Compare the complete compute system.
A GPU server is more than its accelerator. Size memory, host resources, storage and networking around the workload you will run.
Inference and serving
Record model size, precision, context length and target concurrency. Model weights are only part of the memory budget: framework allocation and runtime caches also consume GPU memory. Validate latency and throughput at realistic request sizes rather than relying on a peak compute specification.
Training and experiments
Allow for optimizer state, gradients, checkpoints and dataset access. Confirm framework support, driver requirements and any multi-GPU topology before ordering. GPU count alone does not establish how a distributed training job will scale.
Data preparation and transfer
CPU preprocessing, host RAM and NVMe reads can limit GPU utilization. Include dataset loading, checkpoint exports and concurrent transfers in the deployment brief. Compare bandwidth commitments with the physical networking capabilities of the selected host.
| Resource | Workload input | What to confirm |
|---|---|---|
| GPU memory | Model, precision, batches and runtime state | Exact accelerator model and usable memory |
| Host CPU and RAM | Preprocessing and concurrent jobs | Memory capacity and supported configuration |
| Storage | Datasets, checkpoints and working files | Capacity, throughput and recovery design |
| Network | Dataset ingress and output transfer | Commitment, physical ports and traffic terms |
Dedicated GPU hardware or cloud GPU?
Compare the full workload cost, access model and operational responsibility. Dedicated hardware can suit repeatable workloads that need a known configuration for a longer term. An elastic cloud service can be useful when demand changes rapidly. Compare utilization, data movement, storage, deployment time and software management rather than GPU-hour price alone. 1PBPS lists term-based configurations; it does not promise cloud-style autoscaling or per-second GPU billing.
Confirm location and deployment requirements.
Share your preferred city, operating system, container framework and access requirements. The catalogue is a planning reference, not a live GPU inventory feed. Staff confirm the actual model, topology, facility and delivery schedule. For large dataset movement, include 100 Gbps or unlimited-transfer requirements in a custom quote, with written network terms and pricing.
A clear path to your server.
Keep your hardware choices, payment and technical requirements connected throughout the order.
Frequently asked questions.
How do I choose a GPU server?
Start with model memory, numerical precision, batch size and concurrency. Then match GPU memory, CPU, host RAM, storage throughput and framework requirements to a representative workload.
Are all GPUs available in every city?
The catalogue lists configurations and regional planning prices. Staff must confirm the exact GPU, topology and location availability before deployment.
Can I pair GPU compute with high bandwidth?
Yes. Configure a standard bandwidth commitment or request a 100 Gbps/unlimited-transfer custom quote. Confirm physical port capability, data pipeline needs and commercial network terms with Sales.