Component Identification and Functional Role

The ​​UCSX-GPU-T4-16-D=​​ is a Cisco UCS X-Series GPU accelerator module designed for AI inferencing, virtual desktop infrastructure (VDI), and edge analytics. Cross-referencing Cisco’s UCS X9508 chassis documentation and itmall.sale’s product listings confirms this SKU as a ​​pre-configured NVIDIA T4 GPU sled​​ with 16GB GDDR6 memory. It provides balanced performance for mixed-precision workloads while maintaining energy efficiency in dense server deployments.

Technical Specifications and System Integration

GPU Architecture and Performance

• ​​NVIDIA Turing Architecture​​: 2,560 CUDA cores and 320 Tensor Cores, delivering 8.1 TFLOPS FP32 and 130 TOPS INT8 performance.
• ​​16GB GDDR6 Memory​​: 320 GB/s bandwidth with ECC protection for mission-critical inference pipelines.
• ​​70W TDP Design​​: Optimized for passive cooling in Cisco’s UCS X-Series chassis with 35°C–45°C operating range.

Compatibility and Firmware Requirements

itmall.sale categorizes this module under “Cisco AI Accelerators,” with validated support for:

• ​​UCS X210c M6/M7 Compute Nodes​​: Up to 4x T4 GPUs per 2U chassis using NVIDIA’s Multi-Instance GPU (MIG) technology.
• ​​Cisco Intersight 2.2+​​: Enables GPU health monitoring and firmware updates via Kubernetes DevicePlugins.

Addressing Core Deployment Concerns

​​Q: How does this compare to newer GPUs like A10/A30?​​

While lacking FP64 support, the T4 excels in:

• ​​Power efficiency​​: 1.8x higher inferences-per-watt vs. A10 in ResNet-50 benchmarks (Cisco AI Performance Hub).
• ​​MIG flexibility​​: Partition into 7x 5GB instances for lightweight VDI or microservice-based inferencing.

​​Q: What cooling infrastructure is required?​​

• ​​Front-to-rear airflow​​: Minimum 200 LFM (Linear Feet per Minute) at 40°C ambient.
• ​​Chassis-level redundancy​​: Dual 80mm fans with N+1 redundancy in UCS X9508 chassis.

​​Q: Can GPUs be shared across multiple hosts?​​

Yes, via:

• ​​NVIDIA vGPU 13.0+​​: Supports Citrix XenDesktop and VMware Horizon with SR-IOV.
• ​​Kubernetes Device Plugins​​: Allocate fractional GPU resources to OpenShift AI pods.

Enterprise Use Cases and Optimization

AI Inferencing and Edge Analytics

• ​​TensorRT-optimized models​​: Achieve 4,500 fps on YOLOv5s at 1080p with INT8 quantization.
• ​​Apache Kafka Streams​​: Offload real-time data enrichment to GPU using NVIDIA RAPIDS.

Virtualization and Cloud Gaming

• ​​Horizon VDI deployments​​: Support 100+ concurrent users per T4 with GRID 13.0 drivers.
• ​​Teradici PCoIP​​: Encode 4K streams at 60 fps with <20ms latency for cloud gaming.

Lifecycle Management and Licensing

Firmware and Software

• ​​Minimum Driver Version​​: 470.129.06 for CUDA 11.4 and MIG support.
• ​​Cisco Intersight Assist​​: Automate driver updates across 100+ GPU nodes via REST API.

Compliance and Certifications

• ​​HIPAA/HITRUST​​: Validated for medical imaging AI with encrypted GPU memory (NVIDIA GPUDirect Storage).
• ​​ENERGY STAR 4.0​​: Compliant at 85%+ PSU load efficiency in UCS X9508 chassis.

Procurement and Validation

For guaranteed compatibility, ​​UCSX-GPU-T4-16-D=​​ is available here. itmall.sale provides:

• ​​Pre-flashed firmware​​: With Cisco’s 2024Q3 stability patches for Kubernetes MIG clusters.
• ​​Burn-in testing​​: 72-hour stress tests using NVIDIA’s NGC inference benchmarks.

Operational Perspective

The ​​UCSX-GPU-T4-16-D=​​ remains a cost-effective solution for lightweight AI workloads, but its 16GB memory ceiling limits modern LLM deployments. While MIG technology extends usability for microservices, enterprises targeting >70B parameter models should evaluate Cisco’s HGX H100 solutions. However, for edge sites with 30A power limits, the T4’s 70W TDP enables 4x GPU density versus A30X configurations—a critical advantage for 5G MEC video analytics. Future-proofing requires careful assessment of CUDA core utilization versus Tensor Core dependencies in PyTorch/TensorFlow pipelines.