820178-001 HPE Nvidia 16GB GDDR5 PCIex16 Tesla M60 GPU

HPE 820178-001 Nvidia Tesla M60 Graphics Accelerator Overview

The HPE 820178-001 NVIDIA Tesla M60 Graphics Accelerator is an enterprise-grade dual-GPU solution optimized for high-density virtual desktop infrastructure (VDI), artificial intelligence (AI), deep learning, high-performance computing (HPC), and graphics-intensive applications. With 16GB of GDDR5 memory, PCIe x16 connectivity, and advanced virtualization capabilities, this graphics accelerator provides exceptional performance for compute-heavy workloads and multi-user environments.

Maxwell Architecture and GPU Design

Nvidia Maxwell GPU Technology

Powered by NVIDIA's Maxwell architecture, the Tesla M60 combines performance with energy efficiency. Maxwell features enhanced Streaming Multiprocessors (SMs), optimized memory controllers, and advanced power management that maximize compute throughput while minimizing power consumption. This architecture ensures the HPE 820178-001 delivers sustained performance for enterprise workloads while maintaining energy efficiency and thermal stability in data center environments.

Dual-GPU High-Density Design

The Tesla M60 features two GPUs on a single accelerator, each equipped with 2,048 CUDA cores and 8GB of GDDR5 memory, delivering a total of 16GB. This configuration allows simultaneous processing of multiple virtual machines or GPU-accelerated workloads, enhancing multi-user deployment capabilities and accelerating parallel computational tasks efficiently.

Thermal Management

Maxwell architecture is optimized for energy efficiency, enabling high-performance computing while reducing operational costs. Passive cooling design relies on server airflow to dissipate heat, ensuring reliable operation under continuous load. The HPE Tesla M60 is ideal for dense rack deployments and enterprise environments requiring efficient thermal management.

Memory and Bandwidth Advantages

16GB GDDR5 Memory for Enterprise Applications

The Tesla M60 provides 16GB of high-speed GDDR5 memory, split between the dual GPUs. This memory allocation allows large virtual desktops, 3D rendering, AI inference, and scientific simulations to operate without performance bottlenecks. Memory partitioning is possible using NVIDIA GRID technology, enabling multiple users or workloads to share GPU resources efficiently.

High Bandwidth Interface

Each GPU has a 256-bit memory interface, delivering high-bandwidth access to memory for compute and graphics workloads. Together, the dual GPUs achieve an aggregate bandwidth of up to 160GB/s, accelerating tasks such as 3D rendering, scientific simulations, AI model training, and multi-user VDI deployments. The high throughput ensures smooth performance under heavy enterprise workloads.

Virtual Desktop Infrastructure Benefits

The HPE 820178-001 excels in VDI environments, where multiple users require GPU acceleration. NVIDIA GRID technology allows administrators to dynamically allocate GPU resources, providing each virtual desktop with consistent performance and responsiveness. This feature is critical for engineering, media, architecture, and other industries where high-resolution 3D visualization and graphical simulations are necessary.

PCIe x16 Connectivity and Server Integration

High-Speed PCIe Interface

The Tesla M60 connects via a PCIe x16 Gen3 interface, ensuring low-latency data transfer between the CPU and GPU. This connection supports demanding graphics workloads and compute-intensive tasks, allowing virtual desktops, AI inference, and HPC applications to access GPU resources efficiently. Full-duplex communication ensures simultaneous read and write operations, improving overall system performance.

Optimized HPE Server Integration

The HPE 820178-001 is designed for seamless integration with HPE ProLiant and BladeSystem servers. It supports HPE management tools, including iLO (Integrated Lights-Out), for real-time monitoring of GPU performance, thermal status, and utilization. This integration ensures enterprise-grade reliability and simplifies deployment in complex data center environments.

Scalability for Multi-Node Deployments

Multiple Tesla M60 cards can be deployed across server racks to create GPU clusters for large-scale AI training, HPC simulations, and high-performance rendering. This scalability allows organizations to expand GPU capacity without major infrastructure changes, enabling flexible and cost-effective workload management in data centers.

Nvidia GRID Virtual GPU Technology

The Tesla M60 supports NVIDIA GRID virtualization, allowing each physical GPU to be divided into multiple virtual GPUs. This enables efficient sharing of GPU resources across multiple virtual machines while providing consistent graphics and compute performance. GRID vGPU ensures low latency and high responsiveness, which is essential for enterprise VDI and virtual workstation deployments.

Multi-User Graphics Acceleration

With its dual GPUs, the HPE 820178-001 can deliver graphics acceleration for dozens of virtual desktops simultaneously. Users experience high-quality 3D graphics, responsive application performance, and smooth frame rates. This is particularly beneficial in industries such as design, simulation, healthcare, and media production, where multiple users require access to GPU-intensive applications concurrently.

Enterprise and Compliance

GRID virtualization enhances security by isolating workloads while sharing GPU resources efficiently. The Tesla M60 integrates with enterprise management solutions to ensure data protection, compliance, and centralized monitoring, which is critical for regulated industries and mission-critical applications.

High-Performance Computing Applications

In HPC environments, the Tesla M60 accelerates computational simulations, scientific modeling, and analytics. Dual GPUs enable parallel processing of large datasets, supporting research and enterprise analytics workflows that demand high compute performance.

Graphics Rendering and Visualization

The HPE Tesla M60 is ideal for rendering 3D models, high-resolution visualizations, and graphics-intensive tasks. It accelerates design, simulation, and media workflows, reducing time to completion and improving productivity across multiple workstations or virtual desktops.

Cooling, Thermal Design

Passive Cooling System

The Tesla M60 utilizes a passive cooling system, relying on server airflow to maintain optimal operating temperatures. Its dual-slot form factor and heat spreaders ensure thermal stability even under continuous heavy workloads, making it suitable for enterprise data center deployments.

Power and TDP Management

The HPE 820178-001 has a thermal design power (TDP) of approximately 300 watts. Energy-efficient operation reduces operational costs while delivering peak GPU performance. Optimized power management ensures reliable and consistent operation in 24/7 enterprise environments.

Reliability and Enterprise Readiness

The Tesla M60 incorporates ECC memory for data integrity and is rigorously tested for continuous operation. HPE ensures that the card meets enterprise reliability standards, making it suitable for critical workloads, virtualized environments, and high-performance compute tasks.

Software and Developer Ecosystem

CUDA and Parallel Computing Libraries

The HPE Tesla M60 supports NVIDIA CUDA, allowing developers to harness GPU acceleration for parallel computing tasks. Pre-optimized libraries such as cuDNN, TensorRT, and cuBLAS enable efficient deployment of AI, HPC, and graphics applications, improving developer productivity and workload performance.

Cloud Integration

The accelerator is compatible with VMware, Citrix, and Microsoft Hyper-V virtualization platforms, supporting private and hybrid cloud infrastructures. It allows organizations to deploy GPU-accelerated virtual desktops, cloud-based rendering solutions, and virtual workstations efficiently.

Integration with Developer Tools

NVIDIA Nsight, GPU deployment tools, and monitoring utilities are fully supported on the Tesla M60. These tools enable developers to analyze performance, optimize AI models, and streamline deployment of GPU-accelerated applications, reducing time to market and enhancing workload efficiency.