• NVIDIA Tesla P40 24GB GDDR5 GPU with green black silver accents
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Cisco

Cisco HX-GPU-P40 | Tesla P40 GPU Accelerator, 24GB GDDR5, 3840 CUDA Cores

HX-GPU-P40

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Description

The Cisco HX-GPU-P40 is an NVIDIA Tesla P40 GPU accelerator designed for Cisco HyperFlex HX-Series servers, delivering exceptional deep learning inference and virtual workstation performance. Built on the Pascal architecture with 3840 CUDA cores and 24GB GDDR5 memory, it provides 12 TFLOPS of single-precision compute performance and 47 TOPS of INT8 inference throughput. This passively cooled, dual-slot PCIe 3.0 x16 card is optimized for AI deployment, graphics virtualization, and GPU-accelerated workloads in data center environments.

Features

NVIDIA Pascal GPU architecture with 3840 CUDA parallel processing cores
- 24GB GDDR5 memory with 346 GB/s bandwidth for large dataset handling
- 12 TFLOPS single-precision (FP32) floating-point performance
- 47 TOPS INT8 inference performance optimized for deep learning deployment
- Enhanced programmability with Page Migration Engine
- NVIDIA GRID support for virtual desktop infrastructure (VDI) and virtual workstations
- Flexible vGPU profiles from 1GB to 24GB for multi-user environments
- Hardware-accelerated video encode/decode engines (2x encode, 1x decode)
- PCIe 3.0 x16 interface for maximum bandwidth
- ECC memory protection for data integrity
- Passive thermal solution optimized for data center rack servers
- Dual-slot form factor for dense server configurations
- Support for CUDA, OpenCL, and DirectX 12 APIs

Warranty

All products sold by XS Network Tech include a 12-month warranty on both new and used items. Our in-house technical team thoroughly tests used hardware prior to sale to ensure enterprise-grade reliability.

All technical data should be verified on the manufacturer data sheets.

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Technical Specifications

FAQs

Technical Specifications

GPU: NVIDIA Tesla P40 (Pascal Architecture)
- CUDA Cores: 3840
- Memory: 24GB GDDR5
- Memory Bandwidth: 346 GB/s
- Memory Interface: 384-bit
- Single-Precision Performance: 12 TFLOPS
- INT8 Operations: 47 TOPS (Tera-Operations Per Second)
- System Interface: PCIe 3.0 x16
- Maximum Power Consumption: 250W TDP
- Thermal Solution: Passive cooling (requires system airflow)
- Form Factor: Dual-slot, full-height
- Power Connector: 8-pin EPS
- Dimensions: 267mm (L) x 111mm (H)
- Compatibility: Cisco HyperFlex HX220c M5, HX220c M5 All Flash, HX240c M5 All Flash
- vGPU Profiles Supported: 1GB, 2GB, 3GB, 4GB, 6GB, 8GB, 12GB, 24GB
- Maximum vGPU Instances: 24 (1GB profile)

FAQs

Q: What Cisco HyperFlex systems is the HX-GPU-P40 compatible with?
A: The HX-GPU-P40 is compatible with Cisco HyperFlex HX220c M5, HX220c M5 All Flash, and HX240c M5 All Flash nodes. It requires a system with adequate PCIe Gen 3 x16 slot support and proper airflow for passive cooling.

Q: What is the primary use case for the Tesla P40 GPU?
A: The Tesla P40 is optimized for deep learning inference workloads, delivering 47 TOPS of INT8 performance for real-time AI deployment. It also excels at virtual desktop infrastructure (VDI) with NVIDIA GRID support, enabling up to 24 concurrent virtual GPU instances for remote workstation and graphics applications.

Q: Does the HX-GPU-P40 require active cooling?
A: No, the HX-GPU-P40 features passive cooling with a heatsink design. However, it requires adequate system-level airflow within the server chassis to operate within thermal limits. It is designed for rack-mount server deployment with proper ventilation.

Q: What power requirements does the Tesla P40 have?
A: The Tesla P40 has a maximum power consumption of 250W TDP and requires an 8-pin EPS power connector. Ensure your server power supply can accommodate the additional 250W load and has the appropriate power cable.

Q: Can the Tesla P40 support multiple users simultaneously?
A: Yes, with NVIDIA GRID vGPU technology, the Tesla P40 can support up to 24 concurrent virtual GPU instances using 1GB profiles, or fewer instances with larger memory allocations (2GB, 4GB, 8GB, 12GB, or 24GB profiles) depending on workload requirements.

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