C9130AXI-A: What Makes Cisco’s Latest Acces
Understanding the C9130AXI-A’s Core Features...
UCSC-GPU-A30= in Cisco’s AI Infrastructure Ecosystem
The UCSC-GPU-A30= is Cisco’s NVIDIA A30-based GPU accelerator, purpose-engineered for UCS C-Series and S-Series servers targeting AI inference, training, and high-performance analytics. Unlike generic GPU solutions, it’s pre-validated with Cisco’s UCS Manager 4.3+ and Intersight AIOps, enabling centralized management of GPU clusters across hybrid cloud environments. Cisco’s AI Infrastructure Reference Architecture confirms this accelerator reduces ResNet-50 inference latency by 53% compared to previous T4-based deployments.
Hardware Architecture and Performance Specifications
- GPU Core: NVIDIA Ampere architecture with 24GB GDDR6 ECC memory (933 GB/s bandwidth)
- Compute Power: 5.2 TFLOPS FP64, 10.3 TFLOPS FP32, 82 TFLOPS TF32 (Tensor Core-accelerated)
- Form Factor: Full-height, full-length (FHFL) PCIe 4.0 x16, 225W TDP with dynamic boost
- Cisco Enhancements: Custom thermal sensors for UCS C4800 ML chassis airflow optimization
Cisco’s integration enables GPU partitioning via MIG (Multi-Instance GPU), allowing seven isolated instances (4x 6GB, 3x 3GB) for multi-tenant AI workloads.
Performance Benchmarks for Enterprise AI
- Inference Throughput: 32,000 images/sec on ResNet-50 (TensorRT 8.4) with INT8 precision.
- Training Efficiency: 18% faster BERT-Large training versus A10 GPUs using DeepSpeed Zero-2 optimization.
- Data Analytics: 4.9M queries/sec on Apache Spark 3.2 with GPU-accelerated SQL operations.
In Cisco’s Healthcare AI Case Study, eight UCSC-GPU-A30= accelerators processed 1.2M radiology images daily with 99.3% uptime in HIPAA-compliant UCS C240 M7 nodes.
Thermal and Power Management
The UCSC-GPU-A30= employs Cisco’s Adaptive Power Throttling (APT), dynamically adjusting GPU clock speeds based on chassis ambient temperature (35–45°C range). In UCS C4800 ML chassis with N+1 power redundancy, it sustains 98% of peak performance at 40°C, outperforming HPE’s DL380 Gen10+ A30 configurations by 22% in sustained workloads.
Compatibility and Software Integration
- Supported Servers: UCS C240 M7, C4800 ML, S3264 storage servers
- Hypervisor Support: VMware vSphere 8.0U1+ with NVIDIA AI Enterprise 3.0, Red Hat OpenShift 4.11
- Critical Firmware: Requires Cisco UCS VIC 15238 mLOM firmware 5.2(3d) for RDMA over Converged Ethernet (RoCE)
Enterprise Purchasing and Support
Authorized partners like itmall.sale supply OEM-certified UCSC-GPU-A30= accelerators with Cisco’s AI Accelerator Pack, including 3-year 24/7 TAC support and NVIDIA AI Enterprise licensing. Volume deployments (10+ units) qualify for Cisco’s GPU Health Monitor integration.
Addressing Key Deployment Challenges
Q: How does MIG partitioning impact vGPU licensing?
A: Cisco’s Intersight automates NVIDIA vGPU license allocation per MIG instance, reducing overprovisioning by 37% in VMware environments.
Q: What’s the maximum GPU density per UCS chassis?
A: UCS C4800 ML supports 8x UCSC-GPU-A30= accelerators with 1600W redundant PSUs, achieving 1.3 PFLOPS FP16 compute density.
Q: Can it coexist with older T4 GPUs in the same cluster?
A: Yes, but requires Kubernetes device plugins (v1.26+) for heterogeneous workload scheduling.
Strategic Value in AI-Driven Enterprises
The UCSC-GPU-A30= transcends hardware specs to become a business enabler. A Tokyo autonomous driving startup reduced model iteration cycles from 14 days to 9 hours by pairing 16 of these GPUs with Cisco’s Nexus 9336C-FX2 switches, achieving 56 Gbps InfiniBand-equivalent throughput via RoCEv2. What most architects miss is its role in TCO optimization: by replacing six legacy T4 nodes with three A30-equipped UCS C240 M7s, enterprises cut power costs by 41% while tripling AI inference capacity.
The silent revolution lies in Intersight’s predictive maintenance – analyzing GPU memory ECC errors to preemptively replace units 72 hours before failure. This isn’t just infrastructure; it’s the bridge between today’s AI aspirations and tomorrow’s business realities.