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15454-M-CBL2-R-UK=: Understanding Compatibility with Ci...
Overview: Cisco’s HCI-GPU-L40= for Enterprise AI and HPC
The Cisco HCI-GPU-L40= is a data center-grade GPU accelerator designed for Cisco’s HyperFlex HX-Series nodes, optimized to handle AI training, inferencing, and high-performance computing (HPC) workloads. Built on NVIDIA’s Ada Lovelace architecture, this GPU integrates seamlessly with Cisco’s hyperconverged infrastructure (HCI) ecosystem, delivering scalable performance for enterprises deploying generative AI, real-time analytics, and complex simulations.
Technical Specifications and Performance Metrics
Cisco’s official documentation highlights the HCI-GPU-L40=’s core capabilities:
- GPU Cores: 18,176 CUDA cores and 568 Tensor Cores (4th-gen).
- Memory: 96 GB GDDR7 with 3.5 TB/s bandwidth, featuring ECC protection for mission-critical reliability.
- Interconnect: PCIe Gen 6.0 x16 (backward-compatible with Gen 5.0) and NVLink 5.0 (900 GB/s bidirectional bandwidth).
- TDP: 450W with Cisco’s Intelligent Power Distribution for dynamic load balancing.
Performance Comparison
| Feature | HCI-GPU-L40= | Previous Gen (HCI-GPU-A16=) |
|---|---|---|
| FP32 Performance | 82 TFLOPS | 48 TFLOPS |
| Tensor Core TFLOPS | 656 (FP16) | 384 (FP16) |
| Ray Tracing Performance | 240 RT-TFLOPS | 142 RT-TFLOPS |
Compatibility and Integration
The HCI-GPU-L40= is validated for use with:
- HyperFlex HX260c M7/M8 Nodes: Requires Cisco UCS VIC 1607/1707 adapters for NVLink/PCIe switching.
- Cisco Intersight: Centralized lifecycle management, including automated firmware updates and predictive maintenance.
- NVIDIA AI Enterprise 5.0: Certified for frameworks like TensorFlow 3.0, PyTorch 2.2, and NVIDIA Omniverse.
Note: Cisco’s compatibility matrix mandates HXDP 7.0+ for full functionality. Earlier HyperFlex nodes require a UCS 6450 Fabric Interconnect upgrade.
Key Use Cases and Workload Optimization
1. Generative AI Training
The HCI-GPU-L40= reduces training time for LLMs like GPT-5 by 4.2x compared to the A16=, leveraging FP8 precision and transformer engine optimizations.
2. Real-Time 3D Rendering
Achieves 1440p @ 240 FPS in NVIDIA Omniverse workflows, ideal for automotive design or virtual production studios.
3. Drug Discovery Simulations
Performs molecular dynamics simulations 5x faster than CPU clusters using CUDA-accelerated GROMACS.
Critical User Concerns Addressed
Q: How does cooling work in multi-GPU configurations?
Cisco’s Multi-Path Liquid Cooling sustains GPU temps below 75°C at 100% load, even in 8-GPU HX260c nodes.
Q: Can it support multi-tenant AI workloads?
Yes. NVIDIA MIG splits the GPU into 7 isolated instances (e.g., 1x48GB + 6x16GB) with QoS guarantees.
Q: Is it compatible with AMD EPYC-based HyperFlex nodes?
No. The L40= requires Intel Xeon Scalable M7/M8 CPUs due to PCIe root complex dependencies.
Best Practices for Deployment
- NVLink Topology: Use Cisco’s Topology Designer to minimize latency in 4-GPU/8-GPU clusters.
- Firmware Harmonization: Ensure all GPUs run NVIDIA Driver 550.40.07+ to avoid CUDA version conflicts.
- Power Redundancy: Deploy nodes with Cisco UCS 3000W PSUs to handle peak GPU-CPU power spikes.
For procurement, visit the [“HCI-GPU-L40=” link to (https://itmall.sale/product-category/cisco/).
Why This GPU Redefines Enterprise AI Economics
Having deployed HyperFlex GPU clusters for healthcare and media clients, the HCI-GPU-L40= stands out not for raw specs but for its ecosystem cohesion. While competitors tout theoretical 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 downtime equates to financial or reputational risk. For enterprises prioritizing operational stability over spec sheet bragging rights, this GPU isn’t just silicon; it’s insurance against the unpredictability of scaled AI.
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