Five Steps to Next-Generation Incident Preparedness and Response

The NVIDIA DGX H200 is a carefully engineered system designed for next-generation AI infrastructure, integrating a convergence of GPUs, networking, memory, CPUs, storage, and power systems. It features 8x H200 GPUs, each with 141 GB HBM3e memory and 4.8 TB/s bandwidth, interconnected by NVLink 4.0 and NVSwitch to create a high-bandwidth compute pool. This architecture is crucial for preventing bottlenecks during the training of large language models (LLMs) and multi-tenant inference. systems are vital for sustaining peak loads and continuous high throughput. This comprehensive component design translates into faster training convergence, lower inference costs, reduced I/O stalls, and seamless distributed scaling for enterprises. Uvation assists clients in optimising these deployments to achieve higher utilisation and return on investment. High-core-count CPUs manage orchestration and I/O, whilst NVMe SSDs with parallel file systems and GPUDirect Storage ensure data-hungry AI workloads are fed efficiently. InfiniBand/Ethernet with RoCE and GPUDirect RDMA enable seamless scaling across multiple nodes for distributed AI. Robust cooling and redundant power

For enterprise AI, inference—not training—determines the economic and operational viability of Large Language Models (LLMs). While training is a one-time cost, inference is perpetual, directly impacting user experience (UX) and overall costs. TensorRT, NVIDIA's deep learning inference SDK, optimises trained models for high-performance, low-latency execution without altering their architecture. It achieves this through capabilities like Layer Fusion, FP8/INT8 Quantization, Kernel Auto-Tuning, Dynamic Batching, and Framework Interoperability (supporting PyTorch, TensorFlow, or ONNX). When paired with the NVIDIA H200 GPU, which features native FP8 Tensor Cores, 141 GB HBM3e Memory, and 900 GB/s NVLink bandwidth, TensorRT delivers significant gains. This combination leads to sub-300ms latency, reduced inference costs, and increased throughput for complex LLM use cases. The aim is to make running LLMs profitable by intelligently scaling performance.

NVIDIA Networking OpenFabrics Enterprise Distribution for Linux (MOFED) is NVIDIA's accelerated network software stack, essential for high-performance AI networking. It enables low-latency, high-throughput, and zero-copy data movement between GPUs, CPUs, and storage using technologies like RDMA, InfiniBand, and RoCE. MOFED is critical for unlocking the full potential of NVIDIA H200 GPUs. While the H200 boasts immense processing power, its performance can be severely bottlenecked by inadequate networking. MOFED ensures fast movement of large data blocks and inference traffic, complementing H200 features like HBM3e and NVLink, and preventing issues like high latency and packet loss in distributed training. Real-world use cases for MOFED in H200 environments include distributed LLM training, multi-tenant inference serving, Retrieval-Augmented Generation (RAG), and high-speed storage integration. Uvation deploys MOFED-optimised H200 clusters with pre-installed drivers and configurations to ensure scalable, production-ready AI infrastructure. MOFED is foundational for H200 investments.

The NVIDIA H200 focuses on power management and redundancy, which are crucial for enterprise-grade Large Language Model (LLM) deployments and operational continuity. Modern LLM workloads require sustained performance but risk downtime from single-point power failures or unbalanced thermal profiles. The H200 incorporates features such as a 700W max power draw, dynamic thermal monitoring, multi-rail power redundancy support, and board-level telemetry integration. True redundancy extends beyond the GPU, involving system-level design like dual-feed power, N+1 cooling, and NVSwitch fabric separation. This approach enhances both uptime and model performance, enabling higher GPU utilisation and safer, longer fine-tuning cycles. Uvation assists enterprises in deploying power-optimised, fault-tolerant H200 systems by integrating telemetry and mapping redundancy, ensuring the H200's capabilities are fully unlocked.

We hit 92% accuracy on our GenAI pilot—and the board still flagged it. Why? Because we’d never quantified the system’s potential for deception, privacy leaks, or autonomy.” — CIO post-mortem from a Uvation client

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