​​Product Overview and Target Workloads​​

The ​​Cisco UCSX-GPU-H100-80=​​ is a ​​NVIDIA H100 Tensor Core GPU​​ optimized for Cisco’s UCS X-Series modular systems, delivering ​​80GB HBM3 memory​​ for enterprise AI and HPC workloads. This Cisco-specific SKU integrates ​​pre-configured firmware​​ for seamless compatibility with UCS Manager and Intersight, enabling deterministic performance in multi-tenant environments. Unlike generic H100 GPUs, it supports ​​Cisco’s Unified Fabric Controller​​ for adaptive bandwidth allocation across UCS 9108 fabric interconnects.

​​Technical Architecture and Hardware Innovations​​

​​GPU Core Configuration​​

• ​​NVIDIA Hopper Architecture​​ with ​​16896 CUDA cores​​ and ​​528 Tensor cores​​
• ​​80GB HBM3 memory​​ at ​​3.35TB/s bandwidth​​ (1.6x A100 80GB bandwidth)
• ​​PCIe 5.0 x16 interface​​ with ​​Cisco DirectPath DMA​​ for 128GB/s host-GPU throughput

​​Enterprise-Optimized Features​​

• ​​Multi-Instance GPU (MIG)​​ partitioning into 7x 10GB instances with isolated clocks
• ​​Hardware-enforced SR-IOV​​ supporting 16 virtual GPUs (vGPU) per physical unit
• ​​Cisco Secure Boot​​ with UEFI firmware validation for regulated industries

​​Performance Benchmarks in AI/HPC Workloads​​

​​Generative AI Training​​

• Trains ​​175B-parameter GPT models​​ 3.2x faster than A100 80GB using ​​FP8 precision​​
• Achieves ​​98% scaling efficiency​​ in 8-GPU clusters with ​​NVLink Switch System​​

​​Inference Acceleration​​

• Processes ​​32,000 queries/sec​​ on BERT-Large with <2ms latency (vs. 18,000 on A100)
• ​​MIG-enabled multi-tenancy​​ isolates healthcare AI models with <5% performance penalty

​​Thermal and Power Management​​

• ​​700W maximum TDP​​ with ​​Cisco’s Dynamic Power Capping​​ (±10% adjustments)
• ​​Liquid-cooling readiness​​ via UCS X9508 cold plate compatibility (50°C coolant inlet)
• ​​Per-MIG power telemetry​​ through UCS Manager 4.9(1c) for SLA enforcement

​​Platform Compatibility and Configuration​​

​​Supported UCS Infrastructure​​

• Requires ​​UCS X210c M7 compute nodes​​ with ​​Cisco UCSX-M7-HD100G mezzanine adapters​​
• Validated with ​​UCS 6454 Fabric Interconnects​​ for NVLink over EDR InfiniBand

​​Firmware and Software Requirements​​

• ​​NVIDIA AI Enterprise 4.0​​ with Cisco-signed drivers for vGPU support
• ​​UCS Manager 5.0(1a)​​ mandatory for MIG resource partitioning

​​Deployment Strategies for AI Clusters​​

​​Large Language Model (LLM) Training​​

• ​​3.2TB/s inter-GPU bandwidth​​ via ​​NVLink Switch System 2.0​​
• ​​Checkpointing optimizations​​ using HBM3’s 7.8μs access latency

​​Hyperconverged AI/ML​​

• ​​VMware vSphere 8.0U2​​ supports 8 vGPUs per VM with 99% hardware utilization
• ​​Red Hat OpenShift AI​​ achieves 1,500 containers/GPU using MIG isolation

​​Licensing and Procurement Considerations​​

The UCSX-GPU-H100-80= requires:

• ​​Cisco Intersight for Compute License​​ for GPU health monitoring
• ​​NVIDIA AI Enterprise 4.0+​​ for MIG/vGPU capabilities

For organizations requiring validated hardware with full support SLAs, [“UCSX-GPU-H100-80=” link to (https://itmall.sale/product-category/cisco/) provides Cisco-certified procurement options.

​​Strategic Insights from Technical Evaluations​​

In comparative testing against AMD’s MI250X, the H100-80 demonstrates ​​72% higher FP8 throughput​​ for transformer models—critical for real-time language translation services. While the 700W TDP demands advanced cooling infrastructure, the ​​MIG-enabled resource slicing​​ allows telecom providers to monetize GPU capacity across 16 tenants simultaneously. Healthcare organizations leveraging federated learning observe ​​89% faster model convergence​​ using NVLink’s 900GB/s inter-GPU bandwidth versus traditional RDMA. However, enterprises must carefully evaluate ​​MIG partition sizes​​ against evolving AI model requirements, as repartitioning requires hardware reboot cycles. The accelerator’s ability to sustain 3.1TB/s memory bandwidth during full-system encryption (via NVIDIA DPX instructions) redefines confidential computing economics for financial institutions.