Graphics card NVIDIA Tesla H200 141GB PCIe HBM3e

NVIDIA Tesla H200 141GB PCIe HBM3e is a data‑center GPU in a PCIe add‑in card form factor engineered for AI training, inference and high‑performance computing. With 141 GB of HBM3e high‑bandwidth memory and architecture tuned for large models and datasets, this card provides consolidated memory capacity and sustained bandwidth for demanding server and workstation workloads. The product is intended for deployment in rack servers, appliance nodes and workstation platforms that require accelerated tensor and matrix computation.

Advantages

The Tesla H200 delivers a high memory capacity combined with HBM3e memory bandwidth to reduce off‑chip transfers and improve throughput for large‑scale models. Its PCIe form factor simplifies integration into common server and workstation platforms without requiring specialized interconnects. The design targets sustained performance for mixed‑precision AI training and inference, as well as parallel scientific and data analytics tasks.

Suitable for

• Training large neural networks that require very large GPU memory pools
• Batch and real‑time inference workloads in data‑center environments
• High‑performance computing tasks such as simulation, linear algebra and molecular modeling
• Data‑analytics pipelines that process large in‑memory datasets
• Server and workstation setups where a PCIe accelerator is preferred over proprietary interconnect cards

When it may not be suitable

This GPU is optimized for data‑center and professional compute workloads; it is not intended as a consumer or desktop gaming card. Systems with strict power, thermal or slot‑space constraints may require evaluation of compatibility before deployment. For small‑scale or single‑GPU desktop tasks, a lower‑capacity or consumer‑oriented GPU may be more cost‑effective.

Key specifications

• Form factor: PCIe add‑in card
• Memory type: HBM3e high‑bandwidth memory
• Memory capacity: 141 GB
• Target applications: AI training, inference, high‑performance computing
• Designed cooling: passive or server‑chassis cooling (requires adequate airflow)

How to use

Install the card into a compatible PCIe slot in a server or workstation that meets the required power delivery and cooling specifications. Ensure the host system BIOS and OS support the GPU and that appropriate drivers and software stacks (CUDA, cuDNN, frameworks) are installed and configured. For multi‑GPU setups, configure interconnect and software to manage memory and workload distribution. Monitor temperatures and power draw during initial testing to confirm stable operation under expected workloads.

What to consider before purchase

• System compatibility: available PCIe slots, chassis space and airflow
• Power budget: PSU capacity and power connectors to support sustained load
• Driver and software support for intended frameworks and libraries
• Cooling strategy: chassis or rack cooling sufficient for passive or dense deployments
• Deployment scale: single‑card versus multi‑card topology and host infrastructure

Usage recommendations

For best results, pair this GPU with server hardware designed for high airflow and reliable power delivery, install the latest vendor drivers and AI framework updates, and validate workloads using representative datasets to size memory and compute needs accurately.