What Is the C-HDD-BLANK= and Why Is It Essent
Defining the C-HDD-BLANK= The C-HDD-B...
Defining the UCSC-GPU-A10= in Cisco’s Compute Ecosystem
The Cisco UCSC-GPU-A10= is a PCIe Gen4 GPU acceleration module designed for Cisco UCS C-Series rack servers, optimized for AI inferencing, virtualization, and high-performance computing (HPC). Built around NVIDIA’s A10 Tensor Core GPU architecture, this module delivers 24 GB GDDR6 memory with 672 CUDA cores and 84 RT cores, achieving 31.2 TFLOPS FP32 performance. Tailored for hybrid cloud environments, it supports NVIDIA’s AI Enterprise software stack while integrating tightly with Cisco Intersight’s management platform for policy-driven resource allocation.
Core Technical Specifications
- GPU Architecture: NVIDIA Ampere-based A10 GPU with 3rd Gen Tensor Cores and FP32 acceleration.
- Memory Bandwidth: 600 GB/s via GDDR6 with ECC protection for mission-critical workloads.
- Form Factor: Full-height, full-length (FHFL) PCIe 4.0 x16 card with 250W TDP.
- Virtualization Support:
- NVIDIA vGPU: 1–8 vGPUs per physical card with 6 GB minimum per instance.
- SR-IOV: Direct hardware passthrough for latency-sensitive applications (<5 μs).
- Cooling System: Dual centrifugal fans with dynamic RPM control (1,800–4,500 RPM) and hot-swappable redundancy.
Target Workloads and Performance Benchmarks
1. AI/ML Inferencing
In healthcare imaging deployments, the UCSC-GPU-A10= achieves 18 ms latency for MONAI-based 3D MRI reconstruction tasks, processing 12,000 slices/hour. Compared to previous-gen T4 GPUs, it delivers 3.2× higher throughput for transformer-based NLP models like Nemotron-H.
2. Virtual Desktop Infrastructure (VDI)
With 8 vGPU profiles, a single card supports 128 concurrent users in Citrix environments at 1080p resolution, maintaining <20 ms frame latency. NVIDIA’s RTX Virtual Workstation (vWS) enables real-time ray tracing for CAD workloads.
3. Video Analytics
Using DeepStream SDK, the module processes 48 streams of 4K H.265 video at 60 FPS with AI-based object detection, achieving 95% accuracy in license plate recognition systems.
Integration with Cisco Intersight and NVIDIA AI Stack
The UCSC-GPU-A10= operates within Cisco’s Full-Stack Observability framework through:
- Dynamic Resource Partitioning: Automatically allocates GPU memory between CUDA and TensorRT workloads based on SLA tiers.
- Predictive Maintenance: Analyzes fan bearing harmonics and thermal drift patterns to predict failures 72+ hours in advance.
- Multi-Cloud Orchestration: Unified GPU resource pools across AWS Outposts (EC2 G5 instances) and on-premises UCS clusters.
Common Configuration Pitfalls:
- Overprovisioning vGPU instances beyond physical memory limits, causing 35–40% performance degradation.
- Mixing A10 and older GPU generations in CUDA workloads, leading to kernel scheduler conflicts.
Comparative Analysis: UCSC-GPU-A10= vs. Industry Alternatives
| Metric | UCSC-GPU-A10= | NVIDIA A100 PCIe | AMD Instinct MI50 |
|---|---|---|---|
| FP32 Performance | 31.2 TFLOPS | 19.5 TFLOPS | 26.5 TFLOPS |
| Memory Capacity | 24 GB GDDR6 | 40 GB HBM2e | 32 GB HBM2 |
| vGPU Support | 8 profiles | 10 profiles | N/A |
| Energy Efficiency | 2.1 TFLOPS/W | 1.8 TFLOPS/W | 1.5 TFLOPS/W |
| Management Ecosystem | Cisco Intersight | Baseboard Management | ROCm Management |
While the A100 offers higher memory bandwidth, the UCSC-GPU-A10= excels in Cisco-integrated environments through hardware-rooted TPM 2.0 security and Intersight’s GPU telemetry APIs.
Thermal and Power Optimization
Cisco’s CoolOps 4.0 technology enables:
- Adaptive Fan Curves: Adjusts RPMs within ±5% of optimal cooling needs, reducing acoustics to 48 dB(A) at 30% load.
- Liquid Cooling Readiness: Compatible with rear-door heat exchangers for data centers targeting PUE <1.1.
- GPU Power Capping: Dynamically limits TDP from 250W to 180W during peak grid demand periods.
Procurement and Lifecycle Strategies
- Workload Profiling: Use Cisco UCS Performance Manager to right-size vGPU allocations—e.g., 4 GB/profile for CAD vs. 6 GB for AI training.
- Refurbished Procurement: Platforms like [“UCSC-GPU-A10=” link to (https://itmall.sale/product-category/cisco/) offer certified units with 3-year warranties at 40–60% cost savings.
- EOL Planning: Align refresh cycles with NVIDIA’s Hopper roadmap (H100 PCIe expected in 2026).
The Paradox of Hardware Abstraction
During a recent smart city deployment, engineers initially allocated all 24 GB VRAM to a single AI model—only to discover 60% of memory idle during inference cycles. By implementing Intersight’s memory-tiering policies (12 GB CUDA + 8 GB TensorRT + 4 GB buffer), they achieved 32% higher GPU utilization while reducing energy costs by $9,000/node annually. This underscores a critical insight: raw compute power means little without intelligent orchestration. The UCSC-GPU-A10= shines not as a standalone accelerator but as a policy-driven service layer in Cisco’s AIOps ecosystem—where operational agility trumps brute-force TFLOPS.
References
- Cisco UCS GPU Accelerator Installation Guide
- NVIDIA A10 Tensor Core GPU Architecture Brief
- Cisco Intersight for Hybrid Cloud Operations Whitepaper