Five Steps to Next-Generation Incident Preparedness and Response

The NVIDIA H200 GPU significantly advances AI performance with its 4.8 terabytes per second (TB/s) memory bandwidth, enabled by 141 GB of next-generation HBM3e. This represents a 76% increase in capacity over H100’s HBM3 and ensures continuous data flow to the Hopper architecture’s Tensor Cores, preventing computational stalls. This substantial bandwidth is critical for today's demanding AI workloads, including Large Language Models (LLMs) with extended context windows, Multi-Modal AI, Retrieval-Augmented Generation (RAG) pipelines, and fine-tuning with large batches. Leveraging the H200’s full potential requires careful architecture and optimisation, such as aligning model parallelism and utilising NVLink/NVSwitch topologies. Proper optimisation dramatically improves sustained GPU utilisation, increases tokens per second, reduces epoch times, and lowers power costs. Companies like Uvation assist enterprises in exploiting this bandwidth ceiling, ensuring peak real-world throughput. Ultimately, memory bandwidth is now a decisive factor in AI compute performance.

NVIDIA SuperNICs are the hidden powerhouse of AI cloud data centres, providing the high-throughput, low-latency networking essential for ultra-scale AI workloads. Traditional networking struggles with AI's demands, causing bottlenecks due to variable latency, scaling complexity, and CPU consumption. SuperNICs, including BlueField-3 (400 Gb/s) and ConnectX-8 (up to 800 Gb/s), are Ethernet accelerators engineered for massive AI environments. They utilise RDMA over Converged Ethernet (RoCE) to bypass the CPU, delivering deterministic low-latency and secure multi-tenant isolation, crucial for large language model (LLM) training and inference. When combined with Spectrum-X Networking Fabric, they boost generative AI network performance by 1.6×. Uvation integrates these SuperNICs to build scalable, secure, and predictable AI infrastructure.

This guide, "NVIDIA H200 Training & Fine-Tuning: From Blank Slate to Business Value," serves as an advanced technical guide for AI engineers, ML teams, CTOs, and solution architects. Its core aim is to demonstrate how to transform raw NVIDIA H200 compute into reliable, production-grade AI outcomes, focusing on maximum performance. The NVIDIA H200 offers advantages like 141 GB HBM3e memory, a Transformer Engine with FP8, and NVLink/NVSwitch, leading to shorter time-to-convergence for pretraining and faster fine-tuning. The guide details how to architect training pipelines covering data, precision, parallelism, optimisers, and I/O, as well as fine-tuning strategies like LoRA/QLoRA and methods to control risks like catastrophic forgetting. Crucially, it emphasises pre-flight readiness to prevent costly failures. Uvation assists in designing this end-to-end recipe, providing architectural solutions, customised playbooks, and benchmark reporting to ensure efficient scaling and delivery of business value.

NVIDIA CUDA Cores are the parallel compute units driving AI and HPC workloads, with the H200 GPU representing their fullest expression. The H200 significantly boosts performance by providing 4.8 TB/s memory bandwidth, 141 GB HBM3e, and FP8 precision, ensuring CUDA Cores are continuously fed and highly utilised. Throughput, not theoretical FLOPs, is the true measure of CUDA Core effectiveness, with H200 enabling up to 380K tokens/sec for 70B FP8 LLMs. Proper architecture and orchestration are critical to keep these cores saturated, avoiding pitfalls like memory fragmentation and outdated builds. When optimised, H200 clusters deliver unmatched performance-to-cost ratios, showing gains of +81% in throughput and -38% in power cost, leading to significant ROI and business outcomes.

The NVIDIA H200 GPU marks a significant leap in high-performance computing (HPC) and AI inference. Featuring 141GB of HBM3e memory and 4.8 TB/s bandwidth, it surpasses the H100 and A100, solving memory bottlenecks common in large language models and scientific simulations. Equipped with NVLink fabric and Gen 2 Transformer Engines, the H200 enables 110X faster performance in real-world applications like genomics, climate modeling, and computational fluid dynamics. Compared to legacy A100 clusters, H200 clusters deliver significantly reduced latency and higher token throughput, lowering cost per user and improving total cost of ownership (TCO). Uvation benchmarks show the H200 achieving up to 11,819 tokens per second in LLaMA 13B inference workloads. For enterprises seeking efficient HPC acceleration, the H200 offers a scalable, memory-optimized solution with turnkey deployment options, helping organizations reduce infrastructure costs while maximizing AI and scientific computing performance.

GPU memory is now the biggest bottleneck in AI workloads, surpassing raw FLOPS, as modern AI depends more on memory bandwidth and size. The NVIDIA H200 significantly advances performance by offering 141 GB HBM3e memory and 5.2 TB/s bandwidth, compared to the H100's 80 GB HBM3 and 3.35 TB/s. This provides LLMs with 76% more memory and 1.5x bandwidth, giving them "breathing room". The H200 enables smoother attention head traversal and reduces token-level latency, for instance, being 44% faster for 128K token windows. It excels in enterprise GenAI inference due to consistent latency, higher session concurrency, and memory-persistent batching. Furthermore, the H200 benefits HPC and FP8 training workloads, increasing throughput for tasks like GPT-3 13B fine-tuning by 1.5x. The H200 is therefore the preferred GPU for memory-heavy AI workloads such as public GenAI and RAG + Vision GenAI, with memory being the new AI performance ceiling.

AI infrastructure is evolving, and the new gold standard isn’t hardware price — it’s AI server cost per user. Traditional CapEx models fall short when it comes to dynamic, inference-heavy workloads like generative AI and LLMs. Enter the H200 GPU, with 141GB of memory and 4.8TB/s bandwidth. It doubles concurrent user capacity over H100 while reducing cost per user by 60%. This shift matters for SaaS platforms aiming to scale sustainably without overprovisioning. Higher memory enables smarter batching, faster response times, and fewer idle resources. Real-world data shows H200 serving 160 users per GPU at just $3.50/user/month, making it the most cost-efficient option. With support from Uvation’s managed IT services, businesses can unlock this performance leap with minimal risk. The bottom line? AI infrastructure should speak the language of user economics — and the H200 makes it possible.

The artificial intelligence revolution isn't just changing how we work; it's fundamentally reshaping enterprise IT budgets. With individual AI server racks commanding $300,000 or more for H100/H200 configurations, organizations face a critical financial crossroads that could determine their competitive future.

As AI applications shift from proof-of-concept to production, inference—not training—becomes the dominant cost center. This pillar post breaks down how enterprise IT leaders can slash AI inference costs while preserving performance and user experience. It explores critical metrics like Cost Per Token (CPT), Goodput, and latency benchmarks, then maps out use case-specific infrastructure planning. The guide dives into NVIDIA’s H100 and next-gen H200 GPU architectures, model concurrency, batching strategies, and advanced techniques like speculative decoding and disaggregated serving. Real-world case studies from Wealthsimple, Perplexity AI, Amdocs, and Let’s Enhance show how deploying NVIDIA's inference stack—Triton, TensorRT-LLM, NIM—results in tangible cost savings and performance gains. Whether you're scaling LLMs in the cloud or optimizing multimodal pipelines, this post offers a practical roadmap to modern inference infrastructure. CIOs get the bottom line: scale smart, benchmark the right things, and future-proof your AI workloads without breaking your budget.

As AI models grow in size and complexity, choosing the right GPU isn't just about clock speeds—it’s about how your infrastructure performs under real-world stress. This blog decodes the benchmarking battle between NVIDIA’s H100 and H200 GPUs, revealing why synthetic metrics alone no longer cut it. While synthetic benchmarks like MLPerf showcase the H200’s raw power—thanks to its 141GB of HBM3e memory and 4.8 TB/s bandwidth—real-world benchmarks tell the story enterprise leaders care about: latency, scalability, and total cost of ownership. The H200 dominates in LLM inference, energy efficiency, and batch processing, but the H100 still offers strong ROI for compute-heavy legacy workloads. Whether you're a startup CTO courting investors or a Fortune 500 CIO preparing for seasonal surges, the right benchmarking strategy will determine your AI stack’s success. This isn’t just a hardware upgrade—it’s a shift in how you evaluate, deploy, and scale generative AI infrastructure.