816492-001 HPE Nvidia 16GB GDDR5 PCIex16 Tesla M60 GPU

HPE 816492-001 Nvidia GDDR5 PCIex16 Tesla M60 Graphics Accelerator Overview

The HPE 816492-001 NVIDIA Tesla M60 Graphics Accelerator is a high-performance dual-GPU solution designed for enterprise data centers, virtual desktop infrastructure (VDI), high-performance computing (HPC), and artificial intelligence (AI) applications. With 16GB of GDDR5 memory, PCIe x16 connectivity, and advanced virtualization support, this graphics accelerator delivers industry-leading performance for compute-intensive tasks, multi-user virtual environments, and graphics-intensive workloads.

Nvidia Maxwell GPU Technology

The Tesla M60 leverages NVIDIA’s Maxwell architecture to provide significant improvements in performance per watt, computational efficiency, and virtualization capabilities. Maxwell architecture introduces enhanced Streaming Multiprocessors (SMs), optimized memory controllers, and improved energy efficiency to handle complex workloads with reduced power consumption. This architecture ensures that the HPE 816492-001 delivers both high compute throughput and energy-efficient operation, making it suitable for dense server environments.

High-Density Compute Capabilities

Featuring two GPUs on a single card, the Tesla M60 can handle multiple virtual machines or VDI instances simultaneously. Each GPU contains 2,048 CUDA cores and 8GB of high-speed GDDR5 memory, enabling enterprise servers to run graphics-intensive applications, AI inference, and HPC simulations efficiently. This dual-GPU configuration allows for a highly scalable solution for multi-user deployments.

Thermal Management

The Maxwell architecture provides significant energy savings compared to previous generations, allowing data centers to reduce operational costs while maintaining performance. Passive cooling and advanced thermal design ensure stable operation even under continuous workloads, while server airflow efficiently manages heat dissipation.

Memory and Bandwidth Advantages

16GB GDDR5 Memory for Multi-User Environments

The Tesla M60 includes 16GB of GDDR5 memory, split evenly between its dual GPUs. This memory configuration allows large virtual desktops, 3D modeling applications, and data-intensive compute tasks to operate without latency or performance degradation. Memory allocation can be partitioned using NVIDIA GRID technology to serve multiple users or workloads simultaneously.

High Bandwidth Memory Interface

Each GPU on the Tesla M60 features a 256-bit memory interface, providing high-bandwidth access to data for compute and graphics workloads. Combined, the dual GPUs achieve an aggregate memory bandwidth of 160GB/s, which accelerates rendering, AI inference, and parallel processing applications. This high throughput ensures smooth operation in demanding enterprise environments.

Benefits for Virtual Desktop Infrastructure (VDI)

By leveraging its high memory capacity and bandwidth, the HPE Tesla M60 allows organizations to support multiple high-resolution virtual desktops on a single card. NVIDIA GRID technology enables GPU resources to be partitioned dynamically, ensuring consistent performance for multiple users. This capability is critical for industries requiring 3D visualization, CAD applications, and graphical simulation across numerous workstations.

PCIe x16 Connectivity and Server Integration

High-Speed PCIe Interface

The HPE 816492-001 connects to servers via a PCIe x16 Gen3 interface, providing high-speed data transfer between the CPU and GPU. This low-latency connection supports demanding graphics and compute workloads, ensuring that virtual desktops, AI models, and HPC tasks can access GPU resources efficiently. Full-duplex communication allows simultaneous reading and writing, enhancing overall system performance.

Seamless HPE Server Compatibility

The Tesla M60 is optimized for integration with HPE ProLiant and BladeSystem servers. It works seamlessly with HPE management tools, including iLO (Integrated Lights-Out), for monitoring GPU performance, temperature, and utilization. This integration ensures enterprise-level reliability and ease of deployment in complex data center environments.

Scalability for Multi-Node Deployments

Multiple Tesla M60 cards can be installed across server racks to create GPU clusters for large-scale rendering, AI training, and HPC computations. This scalability allows organizations to expand GPU capacity without significant changes to existing infrastructure, enabling cost-effective growth and flexible workload management.

Nvidia GRID Technology

The Tesla M60 supports NVIDIA GRID virtualization technology, enabling each physical GPU to be divided into multiple virtual GPUs. This allows multiple users to share GPU resources effectively, providing high-quality graphics and compute performance in virtual desktop or cloud environments. GRID vGPU ensures consistent performance and predictable latency across multiple workloads.

Multi-User Graphics Acceleration

With dual GPUs, the HPE Tesla M60 can deliver acceleration for dozens of virtual desktops or applications simultaneously. Users experience smooth 3D graphics, responsive applications, and high frame rates, making it ideal for industries such as engineering, architecture, and media production, where multiple users require access to GPU-accelerated workloads concurrently.

Enterprise Compliance

GRID virtualization enhances security by isolating each virtual machine while allowing access to GPU resources. The HPE Tesla M60 integrates with enterprise management solutions to ensure data protection, compliance, and centralized monitoring, which are essential for regulated industries and mission-critical applications.

High-Performance Computing Workloads

For HPC applications, the Tesla M60 provides exceptional performance in simulations, scientific modeling, and financial calculations. Its dual GPUs allow high-throughput execution of parallel workloads, ensuring efficient computation for research and enterprise analytics.

Graphics Rendering and Visualization

The HPE 816492-001 is optimized for rendering complex 3D models, high-resolution visualizations, and computational graphics tasks. Industries such as design, media, and architecture benefit from accelerated workflows, faster rendering times, and the ability to handle multiple simultaneous graphical workloads.

Cooling and Thermal Design

Passive Cooling for Data Center Environments

The Tesla M60 features a passive cooling design that leverages server airflow for heat dissipation. This approach ensures quiet operation and maintains thermal stability even under continuous heavy workloads. The dual-slot design and optimized heat spreaders enhance temperature management in dense server configurations.

TDP Management

The HPE 816492-001 operates with a thermal design power (TDP) of approximately 300 watts per dual-GPU card, balancing high-performance output with energy efficiency. Power-efficient operation is essential for minimizing data center energy costs while sustaining peak performance.

Enterprise Reliability

HPE and NVIDIA ensure the Tesla M60 meets enterprise reliability standards, with ECC memory for data integrity and robust component testing for continuous operation. This reliability makes the card suitable for critical workloads, 24/7 VDI environments, and high-performance compute tasks.

Software and Ecosystem

CUDA and Parallel Computing Libraries

The Tesla M60 supports NVIDIA CUDA for parallel programming, allowing developers to harness GPU acceleration for complex computations. Pre-optimized libraries and development tools enable efficient AI, HPC, and graphics application deployment across enterprise servers.

Cloud Compatibility

The card is compatible with VMware, Citrix, and Microsoft Hyper-V environments, providing flexibility for private and hybrid cloud infrastructure. Organizations can deploy GPU-accelerated desktops, cloud gaming solutions, and virtual workstations efficiently using the Tesla M60.

Developer Tools and Integration

NVIDIA’s suite of developer tools, including cuDNN, TensorRT, and Nsight, are fully supported. These tools optimize machine learning models, analyze GPU performance, and simplify the deployment of accelerated applications, reducing development time and improving workload efficiency.