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

The NVIDIA B300, or Blackwell Ultra, is engineered for massive AI workloads, featuring 288 GB of HBM3e memory and a 50% increase in compute performance over its predecessor. Its architecture addresses data bottlenecks through NVLink 5, which provides 1.8 TB/s of internal bandwidth per GPU.  For multi-node scaling, B300 systems utilise 800 Gb/s InfiniBand or Ethernet connectivity via ConnectX-8 adapters. These capabilities are delivered through the DGX B300 turnkey appliance and the modular HGX B300 platform. Together, they facilitate large-scale model training and high-speed inference by ensuring compute power is not idled by slow data movement.  Think of the B300 as a high-performance racing engine; without a wide, high-speed highway (the network), it cannot reach its top speeds when working as part of a fleet. 

The NVIDIA B300, or Blackwell Ultra, is engineered for massive AI workloads, featuring 288 GB of HBM3e memory and a 50% increase in compute performance over its predecessor. Its architecture addresses data bottlenecks through NVLink 5, which provides 1.8 TB/s of internal bandwidth per GPU. For multi-node scaling, B300 systems utilise 800 Gb/s InfiniBand or Ethernet connectivity via ConnectX-8 adapters. These capabilities are delivered through the DGX B300 turnkey appliance and the modular HGX B300 platform. Together, they facilitate large-scale model training and high-speed inference by ensuring compute power is not idled by slow data movement. Think of the B300 as a high-performance racing engine; without a wide, high-speed highway (the network), it cannot reach its top speeds when working as part of a fleet.

The NVIDIA Blackwell Ultra (B300) defines a new standard for AI infrastructure, shifting the industry focus from merely adding more GPUs to maximizing efficiency, measured by tokens-per-watt and cost-per-million-tokens. B300 achieves dramatic performance gains over Hopper (7.5× dense throughput) by transitioning to a dual-die unified GPU architecture (208B transistors) and introducing the inference-optimized NVFP4 precision format. The platform is designed to scale as an "AI fabric" via the NVL72 system, where 72 GPUs operate as a single logical computer, achieving 1.1 exaFLOPS of FP4 compute. Although B300 requires Direct Liquid Cooling (DLC) due to its 1,400W power density, this shift ultimately lowers OpEx through increased cooling efficiency. Economically, this efficiency enables systems like the GB200 NVL72 to deliver returns as high as 15× the initial investment.

The NVIDIA DGX B300, launched in March 2025 and built on the Blackwell Ultra architecture, is an advanced AI infrastructure designed to handle complex reasoning, real-time inference, and generative AI workloads simultaneously. It supports the entire AI lifecycle—training, fine-tuning, and inference—on a single platform, reducing delays and fragmentation. The B300 features eight Ultra GPUs with 288 GB of HBM3e each, totaling 2.3 TB across the system, enabling high throughput for models processing extremely long context windows. Data flow is managed by a fifth-generation NVLink internal fabric (14.4 TB/s aggregate bandwidth) and external ConnectX-8 SuperNICs (up to 800 Gb/s) for multi-node clustering. To maintain performance, the system separates AI compute from infrastructure control. A BlueField-3 DPU handles networking, storage, and security tasks, ensuring the Ultra GPUs focus purely on model execution. The operational backbone is managed by software layers like Mission Control, NVIDIA AI Enterprise, and the Dynamo inference layer. Access to the B300 is streamlined through the Uvation Marketplace, which offers configurations and deployment guidance

The B300 GPU is optimized explicitly for Generative AI and complex reasoning workloads, depending on the mandatory B300 Software Stack to maximize low-precision performance like NVFP4 and manage its dual-die hardware. The Foundational Infrastructure layer runs on NVIDIA DGX OS and requires CUDA Toolkit 13.1 or later. A key innovation is NVIDIA CUDA Tile, which updates the programming model to abstract hardware complexity, letting developers use logical data "tiles" for improved performance and code portability. Specialized APIs, including MLOPart and Static SM Partitioning, enable predictable multi-tenancy and efficient resource isolation. The stack also includes accelerated frameworks, such as TensorRT-LLM, and orchestration tools like NVIDIA Mission Control and AI Enterprise, providing a production-grade foundation for large-scale GenAI deployment.

The Dell PowerEdge XE9680 is a flagship 8-GPU, 6U server engineered to overcome infrastructure bottlenecks and move enterprises from experimental AI to full-scale production. It is built around dual 4th or 5th Gen Intel Xeon Scalable processors and supports up to 4TB of DDR5 memory with PCIe Gen 5.0 I/O. A key advantage is its flexible accelerator ecosystem, allowing choice between NVIDIA (H100/H200), AMD (MI300X), or Intel (Gaudi 3) GPUs without requiring a platform redesign. Performance benchmarks show up to 1.8× faster BERT pre-training and 2× higher inference throughput, demonstrating minimal communication bottlenecks and sustained utilization. The XE9680 provides operational efficiency; for example, AMD configurations offer 10–20% acquisition savings, enabling organizations to balance cost and performance for diverse AI workloads. Security is maintained through a Cyber Resilient Architecture and TPM 2.0.

The NVIDIA H200 NVL GPU redefines enterprise AI inference performance by focusing on higher throughput and efficiency for complex workloads like large language models and computer vision systems. The architecture features a substantial upgrade to 141 GB of HBM3e memory with 4.8 TB/s bandwidth, enabling larger AI models to fit entirely within GPU memory, minimizing latency and the need for partitioning. The H200 NVL utilizes fourth-generation NVLink for direct GPU communication up to 900 GB/s, crucial for efficient multi-GPU scaling in generative AI deployments. MLPerf Inference benchmarks confirmed the H200 NVL's advancements, demonstrating up to 1.8x higher performance in LLM inference compared to the H100 PCIe configuration. Furthermore, it offers superior performance-per-watt, resulting in lower energy and infrastructure costs for enterprises scaling their AI services. The NVL form factor is specifically designed for inference and provides flexible deployment options, excelling in recommendation systems and mixed workload environments

Redfish API is the industry standard for modern data centre and infrastructure management, developed by the DMTF to replace older, less secure protocols like IPMI. It leverages a RESTful API model, HTTPS for secure communication, and JSON for human-readable data, facilitating easier interaction for administrators and automation tools. NVIDIA has integrated Redfish API support into its H200 GPU systems via the Baseboard Management Controller (BMC), enabling comprehensive remote management, monitoring, and automation. This allows for efficient handling of user accounts, power control, detailed sensor telemetry, and streamlined firmware updates. Redfish is superior to IPMI due to its enhanced security, standardisation, extensibility, and suitability for scalable, cloud-native environments. For the H200, Redfish optimises energy consumption, enhances diagnostics, and ensures reliable deployment of GPU-rich clusters for AI and HPC workloads.

NVIDIA’s H200 GPU lineup offers two powerhouse options: the H200 SXM and H200 NVL, both equipped with 141GB of ultra-fast HBM3e memory. The SXM variant is designed for multi-GPU scalability in standard HGX servers—ideal for compute-heavy or horizontally scaled workloads. In contrast, the NVL module merges two GPUs into one logical device with 282GB unified memory via NVLink, optimized for massive AI models like GPT-4 that demand vertical scaling and reduced latency. This blog breaks down their differences across memory architecture, bandwidth, cooling requirements, and real-world performance. The H200 NVL shines in memory-bound inference and real-time analytics, while the H200 SXM delivers better cost efficiency and infrastructure compatibility for traditional HPC and modular AI clusters. Ultimately, the right choice depends on your model size, scaling strategy, and infrastructure budget.

The Mellanox Spectrum-2 MSN3700 is NVIDIA’s 32-port 200GbE spine switch designed for high-performance AI, cloud, and telecom data centers. Built on the Spectrum-2 ASIC, it delivers 6.4 Tbps switching capacity and processes over 8.33 billion packets per second—all within a 1U form factor. Its open networking architecture supports Cumulus Linux, SONiC, and native Linux, giving IT teams unmatched flexibility while avoiding vendor lock-in. Features like “What Just Happened” (WJH) telemetry and digital twin simulation via NVIDIA Air simplify troubleshooting and accelerate deployment. The MSN3700 integrates seamlessly with ConnectX SmartNICs, BlueField DPUs, and NetQ orchestration to power everything from AI training clusters to 5G RANs. For telecom-grade needs, the SN3750-SX variant adds PTP, SyncE, and secure boot. This switch isn’t just fast—it’s adaptable, secure, and future-ready, making it a strategic backbone choice for next-gen data infrastructure.