Reducing the Carbon Footprint: Energy-Saving Strategies for Data Centers

The NVIDIA DGX H200 significantly advances enterprise AI infrastructure compared to the DGX H100, focusing on computational throughput and efficiency. The H200 system features eight H200 GPUs, each equipped with 141GB of HBM3e memory, representing a 76% capacity increase over the H100’s 80GB HBM3. This upgrade results in a 43% higher memory bandwidth (4.8 TB/s) and doubles the NVLink interconnect speed to 1.8 TB/s. Benchmarking shows the DGX H200 is 1.8x faster for training Large Language Models exceeding 70 billion parameters. Additionally, the H200 delivers significantly better performance per watt, which reduces energy consumption, cooling needs, and the Total Cost of Ownership (TCO). The DGX H200 also offers an efficient upgrade path, providing deployment continuity with upcoming DGX B200 systems.

The NVIDIA DGX B200, leveraging the groundbreaking Blackwell architecture, is the universal foundation for your enterprise AI factory. This 10U supercomputer delivers unprecedented performance, achieving up to 3X faster training and a massive 15X faster inference compared to the DGX H100. It packs 1,440 GB of unified GPU memory and utilizes the new FP4 precision to hit 144 petaFLOPS for inference. Built with eight Blackwell GPUs, each featuring 208 billion transistors, the B200 tackles models up to 10 trillion parameters. It dramatically improves cost efficiency, lowering the cost per million tokens by 15X, ensuring agility, resilience, and hyperscale efficiency for demanding AI workloads, notably as the modular block for the DGX SuperPOD.

The NVIDIA DGX H200 is recognized as the best AI training server for large-scale enterprise workloads and AI factories, delivering extraordinary computational power for models spanning billions of parameters. Built on the NVIDIA Hopper Architecture, it features eight H200 Tensor Core GPUs. A measurable generational leap over the DGX H100, the H200 utilizes HBM3e memory, offering 1.6 TB of total system memory (a 150% increase) and 4.8 TB/s bandwidth (a 43% increase). It employs NVLink 5.0 and NVSwitch integration to achieve 900 GB/s GPU-to-GPU bandwidth, creating a unified 1.1 TB/s memory pool essential for training massive Generative AI and LLMs. The system comes preconfigured with the NVIDIA AI software stack for operational simplification and is scalable within the DGX SuperPOD architecture for exascale performance. Its enhanced efficiency helps optimize the total cost of ownership (TCO) for enterprises.

The blog "DGX B200 vs DGX H100 Benchmarks: A Deep Dive into NVIDIA’s Next-Gen AI Performance" compares the performance of the DGX H100 (Hopper architecture) against the new DGX B200 (Blackwell architecture) for handling complex AI models. The DGX B200 system uses eight Blackwell B200 Tensor Core GPUs, featuring 192GB of HBM3e memory per GPU, a dual-die design, and NVLink 5.0 connectivity, which doubles the GPU-to-GPU bandwidth to 1.8TB/s (up from the H100’s 900GB/s NVLink 4.0). Benchmarks show that the DGX B200 provides substantial performance improvements: it offers up to three times faster training throughput for large language models and up to 15 times higher performance for inference compared to the H100. Furthermore, Blackwell enhances energy efficiency, potentially achieving up to 30x better efficiency for inference. The B200 also excels in scalability, supporting up to 576 maximum cluster GPUs through enhanced NVLink memory coherence, positioning it as the new benchmark for foundation model training and large-scale simulation tasks.

When NVIDIA introduced the Hopper H200, the question wasn’t just about raw specifications. It was about how its compute cores actually perform in real-world workloads. Could they handle massive AI models without slowing down? Could they keep up in applications that require fast responses, like live AI inference or large-scale scientific simulations?

The comparison of the NVIDIA H200 and AMD MI300X highlights a rivalry between established dominance and emerging open architectures. The H200 is built on the Hopper architecture with 141 GB HBM3e memory and ~989 TFLOPS (FP16), while the MI300X uses CDNA 3, offering greater capacity (192 GB HBM3) and compute performance (~1300 TFLOPS). The NVIDIA H200 provides refined performance and relies on its mature, stable CUDA ecosystem (TensorRT, cuDNN), giving it a lead in large-scale AI training and pure AI parallel tasks. It is favoured by enterprises for reliability. The AMD MI300X emphasizes memory capacity and bandwidth (5.3 TB/s). It performs strongly in inference for memory-intensive LLMs and mixed HPC-AI workloads due to its APU configuration. Utilizing the ROCm platform, the MI300X offers better cost-to-performance value and open architecture freedom, appealing to startups and cost-sensitive deployments. The best choice ultimately depends on specific workload needs and preference for ecosystem stability versus flexibility.

Computational Fluid Dynamics (CFD) has moved from being a specialist’s tool to becoming a hallmark of modern engineering and research. From fine-tuning the aerodynamics of a Formula 1 car to simulating complex blood flow patterns in healthcare, engineers now rely on CFD for insights that are both fast and precise. But delivering this level of fidelity comes at a steep computational cost, one that traditional CPU clusters and earlier GPUs often struggle to handle. The NVIDIA DGX H200 meets this demand head-on, bringing together next-generation GPU performance and AI integration to accelerate large-scale CFD workloads, improve accuracy, and make scaling more seamless than ever before.

The NVIDIA H200 Tensor Core GPU is designed to address memory capacity and bandwidth bottlenecks crucial for Large Language Models (LLMs). Utilizing HBM3e memory, it provides 141 GB of GPU memory and a 4.8 TB/s bandwidth, representing a 1.4x increase over the H100. This capacity facilitates the serving of models with over 100 billion parameters and delivers up to 1.9x faster LLM inference. For distributed AI, the H200 leverages high-speed NVLink and NVSwitch interconnects. However, scalability requires addressing collective communication challenges and dynamic workload skew, particularly in MoE models, often through sophisticated, communication-aware schedulers. The high computational density demands up to 10.2 kW for a DGX system, necessitating liquid cooling to prevent thermal imbalance and subsequent clock throttling. Despite the complexity, the H200 promises up to 50% reduced TCO for LLM inference due to its superior power efficiency. The H200 era emphasizes the need for full-stack optimization and intelligent hardware-software co-design.