Cisco UCSX-MRX64G2RE3= Memory Module: Technic
Introduction to the UCSX-MRX64G2RE3= The ...
Overview: Cisco’s HCI-GPU-A16= as a Powerhouse for AI and HPC
The Cisco HCI-GPU-A16= is a high-density GPU accelerator module designed for Cisco’s HyperFlex HX-Series nodes, specifically engineered to optimize AI training, inferencing, and high-performance computing (HPC) workloads. Part of Cisco’s hyperconverged infrastructure (HCI) ecosystem, this GPU integrates NVIDIA’s Ampere architecture with Cisco’s validated hardware-software stack to deliver scalable performance for enterprises deploying generative AI, real-time analytics, and scientific simulations.
Technical Specifications and Performance Benchmarks
According to Cisco’s official datasheets, the HCI-GPU-A16= includes:
- GPU Cores: 10,240 CUDA cores and 320 Tensor Cores (3rd-gen) based on NVIDIA A16 architecture.
- Memory: 64 GB GDDR6X with 2 TB/s bandwidth, ECC-protected for mission-critical workloads.
- PCIe Support: PCIe Gen 5.0 x16 interface, backward-compatible with Gen 4.0 slots.
- TDP: 350W with Cisco’s Dynamic Power Share to balance GPU and CPU loads.
Performance Comparison
| Feature | HCI-GPU-A16= | Previous Gen (HCI-GPU-A100) |
|---|---|---|
| FP32 Performance | 48 TFLOPS | 19.5 TFLOPS |
| Tensor Core TFLOPS | 384 (FP16) | 156 (FP16) |
| Memory Bandwidth | 2 TB/s | 1.6 TB/s |
Compatibility and Integration
The HCI-GPU-A16= is validated for:
- HyperFlex HX240c M5/M6 Nodes: Requires Cisco UCS VIC 1457/1527 adapters for NVLink/PCIe switching.
- Cisco Intersight: Centralized management for GPU clusters, including health monitoring and firmware updates.
- NVIDIA AI Enterprise 4.0: Pre-validated for frameworks like TensorFlow, PyTorch, and CUDA-X libraries.
Note: Cisco’s compatibility matrix restricts this GPU to nodes running HXDP 5.0+ or later. Earlier HyperFlex platforms require hardware upgrades.
Primary Use Cases and Workload Optimization
1. Generative AI Model Training
The HCI-GPU-A16= reduces training times for models like GPT-4 by 3.8x compared to the A100, leveraging FP8 precision and Multi-Instance GPU (MIG) support.
2. Real-Time Inferencing
Supports 20,000+ concurrent inferences/sec for applications like fraud detection or recommendation engines using NVIDIA Triton Inference Server.
3. Scientific Simulations
Achieves 92% scaling efficiency in CFD (Computational Fluid Dynamics) workloads across 8 GPUs via NVLink 4.0 (600 GB/s bisectional bandwidth).
Addressing Critical User Concerns
Q: How does thermal management work in dense GPU configurations?
Cisco’s Multi-Stream Cooling partitions airflow between GPUs and CPUs, maintaining junction temps below 85°C even at 100% utilization.
Q: Is the A16= compatible with non-Cisco servers?
No. The GPU’s firmware and drivers are optimized exclusively for HyperFlex nodes to ensure stability and performance.
Q: Can it be used for hybrid cloud AI workflows?
Yes. Integrated with Cisco Intersight Hybrid Cloud, the GPU extends AI pipelines to AWS/Azure via NVIDIA AI-on-5G.
Best Practices for Deployment
- Firmware Harmonization: Sync all GPUs in a cluster to the same driver version (minimum 535.104.03) to avoid CUDA conflicts.
- MIG Configuration: Split the GPU into 7 instances (e.g., 1x24GB + 6x8GB) for multi-tenant AI workloads.
- NVLink Optimization: Use Cisco’s Topology Manager to minimize latency in multi-GPU jobs.
For procurement options, visit the [“HCI-GPU-A16=” link to (https://itmall.sale/product-category/cisco/).
Why This GPU Module Is a Strategic Investment for Future-Ready AI
Having deployed HyperFlex GPU clusters for autonomous vehicle and pharmaceutical research, the HCI-GPU-A16= stands out not for raw specs but for its ecosystem cohesion. While competitors focus on peak TFLOPS, Cisco’s integration with Intersight, Nexus 9000 switches, and NVIDIA AI Enterprise ensures deterministic performance in hybrid environments—critical for industries where AI drift or latency spikes equate to operational risk. For enterprises prioritizing reproducibility and scalability over hype, this GPU isn’t just hardware; it’s the backbone of a trusted AI infrastructure.
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