P53607-001 HPE Nvidia Tesla A10M 20GB GDDR6 GPU

Main Details of HPE 20GB GDDR6 GPU

The HPE P53607-001 Nvidia Tesla A10M 20GB GDDR6 GPU is a professional-grade graphics accelerator designed for advanced computing, machine learning, and high-resolution rendering tasks.

Manufacturer Information

• Brand Name: HPE
• Model Number: P53607-001
• Product Type: Graphics Card

Technical Highlights

GPU Architecture & Performance

• Architecture: Nvidia Tesla A10M
• Base Frequency: 885 MHz
• Boost Frequency: 1695 MHz
• Memory Clock Speed: 1563 MHz

Memory Details

• Memory Type: GDDR6
• Installed VRAM: 20 GB
• Bus Width: 256-bit
• Bandwidth: 400 GB/s

Connectivity & Expansion

• Interface: PCI Express 4.0 x16

Power Requirements

• Maximum Power Draw: 150W
• Connector Type: 8-pin PCIE

HPE P53607-001 Nvidia Tesla A10M 20GB GDDR6 GPU Overview

The HPE P53607-001 NVIDIA Tesla A10M 20GB GDDR6 PCIe Graphics Card belongs to the enterprise-grade GPU acceleration category designed for modern data centers, cloud platforms, and virtualization-heavy environments. This category focuses on delivering consistent, scalable graphics and compute performance for professional workloads that demand reliability, efficiency, and compatibility with server-class infrastructure. Unlike consumer graphics solutions, enterprise GPUs are engineered for continuous operation, predictable thermal behavior, and long-term platform support within rack-mounted and blade server ecosystems.

Within this category, acceleration hardware is optimized to support mixed workloads that include virtual desktop infrastructure, AI-assisted applications, rendering pipelines, simulation models, and data analytics. The Tesla A10M class emphasizes balance, providing strong performance per watt while maintaining compatibility with PCIe-based server architectures. This makes it particularly relevant for organizations seeking to consolidate workloads while maintaining performance isolation and operational stability.

Positioning of the NVIDIA Tesla A10M in the HPE Ecosystem

The HPE P53607-001 variant represents a vendor-certified implementation of the NVIDIA Tesla A10M GPU, integrated into Hewlett Packard Enterprise server platforms. This category positioning ensures firmware alignment, validated drivers, and mechanical compatibility with HPE ProLiant and related enterprise systems. The result is a solution that aligns with HPE’s broader infrastructure strategy, supporting lifecycle management, remote monitoring, and enterprise support agreements.

From a category perspective, HPE-certified GPUs are intended to reduce deployment risk. They are tested for airflow characteristics, power draw, and thermal stability within specific chassis configurations. This level of validation is essential for data centers where uptime, predictability, and serviceability are critical. The Tesla A10M 20GB card fits squarely into this category by offering a standardized accelerator that can be deployed at scale.

Architecture and Core Technology Characteristics

The NVIDIA Tesla A10M GPU category is based on modern GPU architecture principles that prioritize parallel processing, memory bandwidth efficiency, and hardware-level virtualization support. The architecture is designed to handle concurrent workloads, allowing multiple users or applications to access GPU resources without contention or instability. This architectural approach is fundamental for enterprises that rely on shared infrastructure models.

At the category level, GPUs such as the A10M emphasize architectural efficiency rather than peak consumer-oriented graphics output. The focus is on sustained throughput, deterministic latency, and compatibility with professional software stacks. These architectural traits allow the GPU to serve as a reliable accelerator across diverse enterprise scenarios.

GDDR6 Memory Subcategory Characteristics

The inclusion of 20GB of GDDR6 memory places the Tesla A10M firmly within a high-capacity memory subcategory of enterprise GPUs. GDDR6 is selected for its balance of bandwidth and power efficiency, enabling large datasets, complex textures, and multiple virtual sessions to reside in GPU memory simultaneously. This capacity is particularly important in virtualized environments where memory oversubscription can negatively impact user experience.

In category terms, GPUs with higher onboard memory are favored for consolidation strategies. They allow more virtual machines or application containers to share a single physical accelerator. This improves hardware utilization and reduces the total number of GPUs required in a data center deployment.

Memory Reliability and Error Management

Enterprise GPU memory subsystems are designed with reliability features that support long-term operation. While consumer graphics cards often prioritize raw speed, enterprise categories emphasize data integrity and predictable behavior. Memory management techniques, optimized drivers, and firmware-level controls help maintain stability even under sustained load.

This reliability focus makes the Tesla A10M category suitable for mission-critical workloads where data accuracy and session continuity are essential. From engineering simulations to financial modeling, the memory subsystem plays a crucial role in overall system trustworthiness.

PCIe Integration and Server Compatibility

The PCIe interface defines a major category distinction between enterprise GPUs and specialized accelerator modules. The HPE P53607-001 Tesla A10M uses a PCIe form factor that allows broad compatibility across a wide range of server platforms. This design choice simplifies upgrades and expansions, enabling organizations to integrate GPU acceleration without redesigning their infrastructure.

PCIe-based GPUs are particularly valuable in modular data centers where flexibility is a priority. Administrators can deploy, replace, or reallocate GPUs as workload demands evolve. This aligns with modern infrastructure strategies that emphasize adaptability and rapid scaling.

Power and Thermal Design Considerations

Within the PCIe GPU category, power efficiency and thermal management are critical differentiators. The Tesla A10M is engineered to operate within defined power envelopes suitable for dense server environments. This ensures that multiple GPUs can be installed in a single chassis without exceeding cooling or power delivery limits.

Thermal consistency is especially important in enterprise racks where airflow is carefully managed. GPUs in this category are designed to maintain stable operating temperatures under continuous load, reducing the risk of thermal throttling or unexpected shutdowns.

Data Center Density Optimization

The category focus on density optimization allows organizations to maximize compute and graphics capability per rack unit. By selecting GPUs with balanced performance and power characteristics, data centers can achieve higher utilization without compromising reliability. The Tesla A10M fits this category by offering acceleration capabilities that scale efficiently in multi-GPU configurations.

Virtualization and Multi-User Workload Enablement

A defining feature of the Tesla A10M GPU category is its suitability for virtualization. Enterprise GPUs are designed to support technologies that allow multiple virtual desktops or applications to share a single physical GPU. This capability is essential for VDI deployments, remote work solutions, and shared compute environments.

Virtualization-friendly GPUs enable consistent user experiences by allocating predictable portions of GPU resources to each session. This contrasts with consumer GPUs, which are not optimized for multi-tenant usage and can exhibit unpredictable performance under shared loads.

VDI and Remote Graphics Subcategory

Within the broader enterprise GPU category, the Tesla A10M aligns closely with the VDI and remote graphics subcategory. This includes workloads such as 3D design, media editing, and data visualization delivered over virtual desktops. High memory capacity and efficient resource scheduling allow these applications to perform smoothly even when accessed remotely.

The category emphasis on remote graphics has grown significantly as organizations adopt hybrid and remote work models. GPUs like the Tesla A10M provide the foundation for responsive, secure, and scalable virtual workspaces.

Session Consistency and User Experience

Consistency is a key metric in this subcategory. Enterprise GPUs are engineered to deliver uniform performance across sessions, minimizing latency spikes and visual artifacts. This is particularly important for professional users who rely on precise rendering and real-time feedback.

AI, Analytics, and Compute Acceleration Use Cases

Beyond graphics, the Tesla A10M category supports a range of compute-accelerated workloads. Modern enterprise GPUs are increasingly used for AI inference, data analytics, and simulation tasks. These workloads benefit from the parallel processing capabilities inherent in GPU architecture.

The category positioning of the Tesla A10M emphasizes versatility. Organizations can deploy a single GPU model to support both visualization and compute tasks, simplifying procurement and management.

Inference and Data Processing Applications

In AI and analytics contexts, GPUs in this category are used to accelerate model inference, pattern recognition, and data transformation tasks. The ability to process large volumes of data in parallel reduces response times and improves overall system throughput.

This makes the Tesla A10M category suitable for edge analytics, enterprise AI services, and real-time decision support systems where predictable performance is required.

Balanced Performance for Mixed Workloads

A key advantage of this category is its ability to handle mixed workloads without specialization overhead. Rather than deploying separate accelerators for graphics and compute, organizations can rely on a unified GPU platform. This balance reduces complexity and improves return on investment.

Software Ecosystem and Driver Support

The enterprise GPU category is closely tied to its software ecosystem. The Tesla A10M benefits from professional-grade drivers, long-term support branches, and compatibility with a wide range of operating systems and hypervisors. This ensures stable operation across diverse deployment scenarios.

HPE-certified GPUs further enhance this category by integrating with system management tools and support frameworks. Administrators can monitor performance, update firmware, and troubleshoot issues using familiar enterprise workflows.

Professional Application Compatibility

Enterprise GPUs are validated against professional software stacks used in engineering, media production, and scientific research. This validation reduces the risk of application incompatibility and ensures predictable performance. The Tesla A10M category supports this requirement by aligning hardware capabilities with certified drivers.

Reliability, Security, and Enterprise Readiness

Reliability and security are foundational attributes of the Tesla A10M category. Enterprise GPUs are built to operate continuously under load, with design considerations that minimize failure rates. This is essential for data centers where unplanned downtime can have significant operational and financial impacts.

Security considerations include firmware integrity, driver signing, and compatibility with secure boot environments. These features align the GPU category with broader enterprise security strategies.

Operational Stability in Mission-Critical Environments

Mission-critical environments demand hardware that behaves predictably over time. The Tesla A10M category is engineered to meet these demands through rigorous validation and quality control. This stability supports use cases ranging from healthcare imaging to financial services analytics.

Scalability and Future-Proofing

Scalability is a core category attribute. As workload demands grow, organizations can scale GPU resources horizontally by adding additional cards or vertically by allocating more virtual resources per GPU. The Tesla A10M fits into this scalable framework, enabling incremental growth without architectural redesign.