HPE NVIDIA A40 Graphic Card - 48 GB GDDR6

HPESKU: 6614691

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Sale price$13,082.60

Description

HPE NVIDIA A40 Graphic Card - 48 GB GDDR6

Introducing the HPE NVIDIA A40 Graphic Card, a powerhouse designed to accelerate AI at scale in data centers and HPC environments. Built around NVIDIA’s advanced Ampere architecture and paired with 48 GB of high-speed GDDR6 memory, the A40 empowers organizations to develop, train, and deploy AI models with unprecedented performance and efficiency. From generative AI and large language models to computer vision, scientific computing, and financial modeling, this GPU accelerates both training and real-time inference across demanding workloads. Seamlessly integrated with HPE Cray systems, the A40 is engineered for reliability, scalability, and multi-tenant workloads, making it an ideal backbone for research labs, enterprises, and mission-critical AI pipelines.

  • Massive 48 GB GDDR6 memory for large-scale AI models: The A40’s expansive memory pool supports training and inference for enormous models and expansive datasets, reducing the need for model sharding and enabling multi-modal, multi-task AI workflows. This generous memory capacity helps you keep larger context windows, run multi-gpu data-parallel training more efficiently, and sustain high-throughput inference for complex computer vision and NLP tasks. ECC-enabled memory ensures data integrity under load, delivering reliable performance in production environments where accuracy and stability matter most.
  • AI acceleration with Tensor Cores for training and inference: The A40 leverages NVIDIA Tensor Cores to accelerate matrix operations across precision modes, delivering dramatic speedups for deep learning training and inference. With support for mixed-precision computing (e.g., FP16/BF16, FP32, and optimized INT8 paths), you can achieve higher throughput without sacrificing model accuracy. This translates into faster experimentation cycles for researchers, tighter deployment loops for data-science teams, and lower latency for real-time AI applications such as vision-based object detection, segmentation, and streaming NLP.
  • Real-time inference and analytics for AI workloads: Designed to power latency-sensitive AI tasks, the A40 delivers consistent, high-throughput inference for generative AI, perception systems, and multi-stream analytics. Whether you’re running video analytics, anomaly detection, or live scoring of financial models, this GPU maintains performance under sustained load. The combination of large memory, robust compute, and optimized software stacks means you can deploy complex pipelines with confidence and deliver near real-time results to end users and automated systems.
  • Enterprise-grade integration with HPE Cray systems: The A40 is built to slot into HPE’s enterprise-grade servers and HPC clusters, benefiting from server-level reliability, power management, cooling, and management tooling. It pairs NVIDIA’s enterprise-grade drivers and CUDA ecosystems with HPE’s robust infrastructure to support multi-user, multi-tenant workloads, high availability, and scalable AI services. This integration simplifies deployment, monitoring, and maintenance across large AI initiatives, empowering teams to run production-grade AI services with predictable performance and reduced downtime.
  • Versatile for research and production across industries: From academic labs to financial services, the A40 supports a wide range of AI workloads, including supervised and unsupervised training for generative AI, large language models, computer vision, and scientific simulations. Its flexibility makes it a solid foundation for AI research platforms, data science environments, and production inference fleets. Organizations can consolidate training, experimentation, and deployment on a single, scalable GPU accelerator, accelerating time-to-insight while delivering cost-efficient scalability for evolving AI strategies.

Technical Details of HPE NVIDIA A40 Graphic Card - 48 GB GDDR6

  • GPU Architecture: NVIDIA Ampere-based accelerator with dedicated Tensor Cores for AI, high-performance compute, and graphics workloads.
  • Memory: 48 GB GDDR6 with ECC for data integrity and reliability during long-running AI jobs and large-scale simulations.
  • Memory Bandwidth: High-bandwidth memory subsystem designed to feed compute units and accelerators with data rapidly, supporting large-scale training and fast inference pipelines.
  • Interface/Form Factor: PCIe 4.0 compliant add-in card suitable for data-center servers and HPE Cray configurations; optimized for server-grade cooling and power delivery.
  • Multi-GPU and Interconnect Readiness: Compatible with enterprise interconnects and scalable configurations in supported data-center environments, enabling efficient multi-GPU deployments and workload partitioning when paired with compatible systems.

How to install HPE NVIDIA A40 Graphic Card

Step 1: Power down the server and unplug all power sources. Ensure you’re working on a grounded surface and follow your data-center safety procedures. Remove the chassis panel or expansion slot cover as required by your server model.

Step 2: Identify a suitable PCIe x16 slot with adequate clearance and cooling. If your system supports PCIe Gen 4 and multi-GPU configurations, select a slot that optimizes airflow and reduces heat buildup for peak performance.

Step 3: Insert the HPE NVIDIA A40 into the PCIe slot until it is firmly seated. If your server chassis requires a retention mechanism, engage it to secure the card in place.

Step 4: Connect any required power cables according to your server’s power distribution guidelines. Ensure power connectors are fully seated and that cable routing does not obstruct fans or airflow.

Step 5: Reinstall the chassis panel, reconnect power, and boot the system. Enter the system BIOS/UEFI to verify PCIe slot configuration and enable any necessary settings for PCIe Gen 4 operation and PCIe slot performance tuning.

Step 6: Install or update NVIDIA drivers and CUDA toolkit using your standard enterprise deployment process. Verify card recognition with a command like nvidia-smi and run a short test workload to confirm stability before moving to full-scale AI workloads.

Step 7: Configure your AI software stack (CUDA-enabled frameworks, containers, and orchestration tools) to utilize the A40. Consider enabling ECC settings and driver-level optimizations for your specific use case and workload mix.

Frequently asked questions

  • Q: What workloads is the HPE NVIDIA A40 best suited for?
  • A: It’s ideal for large-scale AI training and inference, generative AI, computer vision, large language models, scientific computing, and financial modeling in data-center environments. The 48 GB of memory and Tensor Core acceleration make it well suited for both research and production workloads requiring high throughput and low latency.
  • Q: Does the A40 support multi-GPU scaling?
  • A: Yes, when deployed in compatible data-center servers and interconnects, the A40 supports scalable multi-GPU configurations. This enables larger model training and more expansive inference pipelines across GPUs in a cluster, complementing HPE Cray system architectures.
  • Q: What software and drivers are required?
  • A: NVIDIA enterprise drivers and the CUDA toolkit are recommended, with integration into your Linux-based or enterprise software stack. For best results, align drivers with your HPC or AI software stack and your organization’s IT governance and security policies.
  • Q: Is ECC memory available on the A40?
  • A: Yes, the 48 GB GDDR6 memory on the A40 includes ECC to protect against data corruption, which is critical for long-running training jobs and production inference at scale.
  • Q: How does the A40 integrate with HPE Cray systems?
  • A: The A40 is designed to work within HPE Cray configurations, benefiting from HPE’s infrastructure management, cooling solutions, power optimization, and validated driver stacks to ensure reliability and predictable performance across large AI deployments.

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