The metaverse is coming, and it's going to change everything.

The NVIDIA DGX BasePOD™ is a pre-tested, ready-to-deploy blueprint for enterprise AI infrastructure, designed to solve the complexity and time-consuming challenges of building AI solutions. It integrates cutting-edge components like the NVIDIA H200 GPU and optimises compute, networking, storage, and software layers for seamless performance. This unified, scalable system drastically reduces setup time from months to weeks, eliminates compatibility risks, and maximises resource usage. The BasePOD™ supports demanding AI workloads like large language models and generative AI, enabling enterprises to deploy AI faster and scale efficiently from a few to thousands of GPUs.

The "NVIDIA H200 vs Gaudi 3" article analyses two new flagship AI GPUs battling for dominance in the rapidly growing artificial intelligence hardware market. The NVIDIA H200, a successor to the H100, is built on the Hopper architecture, boasting 141 GB of HBM3e memory with an impressive 4.8 TB/s bandwidth and a 700W power draw. It is designed for top-tier performance, particularly excelling in training massive AI models and memory-bound inference tasks. The H200 carries a premium price tag, estimated above $40,000. Intel's Gaudi 3 features a custom architecture, including 128 GB of HBM2e memory with 3.7 TB/s bandwidth and a 96 MB SRAM cache, operating at a lower 600W TDP. Gaudi 3 aims to challenge NVIDIA's leadership by offering strong performance and better performance-per-watt, particularly for large-scale deployments, at a potentially lower cost – estimated to be 30% to 40% less than the H100. While NVIDIA benefits from its mature CUDA ecosystem, Intel's Gaudi 3 relies on its SynapseAI software, which may require code migration efforts for developers. The choice between the H200 and Gaudi 3 ultimately depends on a project's specific needs, budget constraints, and desired balance between raw performance and value.

In the AI era, data sovereignty (legal control based on location), residency (physical storage choice), and localization (legal requirement to keep data local) are critical yet complex concepts. Their interplay significantly impacts AI development, requiring massive datasets to comply with diverse global laws. Regulations like GDPR, China’s PIPL, and Russia’s Federal Law No. 242-FZ highlight these challenges, with rulings such as Schrems II demonstrating that legal agreements cannot always override conflicting national laws where data is physically located. This leads to fragmented compliance, increased costs, and potential AI bias due to limited data inputs. Businesses can navigate this by leveraging federated learning, synthetic data, sovereign clouds, and adaptive infrastructure. Ultimately, mastering these intertwined challenges is essential for responsible AI, avoiding penalties, and fostering global trust.

Artificial intelligence is transforming industries, but its complex models demand specialized computing power. Standard servers often struggle. That’s where NVIDIA DGX systems come in – they are pre-built, supercomputing platforms designed from the ground up specifically for the intense demands of enterprise AI. Think of them as factory-tuned engines built solely for accelerating AI development and deployment.

The NVIDIA H200 GPU marks a groundbreaking leap in high-performance computing (HPC), designed to accelerate scientific breakthroughs. It addresses critical bottlenecks with its unprecedented 141GB of HBM3e memory and 4.8 TB/s memory bandwidth, enabling larger datasets and higher-resolution models. The H200 also delivers 2x faster AI training and simulation speeds, significantly reducing experiment times. This powerful GPU transforms fields such as climate science, drug discovery, genomics, and astrophysics by handling massive data and complex calculations more efficiently. It integrates seamlessly into modern HPC environments, being compatible with H100 systems, and is accessible through major cloud platforms, making advanced supercomputing more democratic and energy-efficient

AI inference is happening everywhere, and it’s growing fast. Think of AI inference as the moment when a trained AI model makes a prediction or decision. For example, when a chatbot answers your question or a self-driving car spots a pedestrian. This explosion in real-time AI applications is creating huge demand for specialized chips. These chips must deliver three key things: blazing speed to handle requests instantly, energy efficiency to save power and costs, and affordability to scale widely.

While NVIDIA H200 servers carry a higher upfront price, they deliver significant long-term savings that dramatically reduce Total Cost of Ownership (TCO). This blog breaks down how H200’s efficiency slashes operational expenses—power, cooling, space, downtime, and staff productivity—by up to 46% compared to older GPUs like the H100. Each H200 server consumes less energy, delivers 1.9x higher performance, and reduces data center footprint, enabling fewer servers to do more. Faster model training and greater reliability minimize costly downtime and free up valuable engineering time. The blog also explores how NVIDIA’s software ecosystem—CUDA, cuDNN, TensorRT, and AI Enterprise—boosts GPU utilization and accelerates deployment cycles. In real-world comparisons, a 100-GPU H200 cluster saves over $6.7 million across five years versus an H100 setup, reaching a payback point by Year 2. The message is clear: the H200 isn’t a cost—it’s an investment in efficiency, scalability, and future-proof AI infrastructure.

Imagine training an AI that spots tumors or predicts hurricanes—cutting-edge science with a side of electric shock on your utility bill. AI is hungry. Really hungry. And as models balloon and data swells, power consumption is spiking to nation-sized levels. Left unchecked, that power curve could torch budgets and bulldoze sustainability targets.

The B2B sales process is always evolving. The advent of Big Data presents new opportunities for B2B sales teams as they look to transition from labor-intensive manual processes to a more informed, automated approach.

obotics engineers are designing and manufacturing more robots that resemble and behave like humans—with a growing number of real-world applications. For example, humanoid service robots (SRs) were critical to continued healthcare and other services during the COVID-19 pandemic, when safety and social distancing requirements made human services less viable,

Across the globe, “5G” is among the most widely discussed emerging communications technologies. But while 5G stands to impact all industries, consumers are yet to realize its full benefits due to outdated infrastructure and a lack of successful real-world cases

It’s more challenging than ever to find viable IT talent. Managed services help organzations get the talent they need, right when they need it. If you’re considering outsourcing or augmenting your IT function, here’s what you need to know about the benefits of partnering with a managed service provider. Managed services can provide you with strategic IT capabilities that support your long-term goals. Here are some of the benefits of working with an MSP.

It all started with the global pandemic that startled the world in 2020. One and a half years later, remote working has become the new normal in several industries. According to a study conducted by Forbes, 74% of professionals expect remote work to become a standard now.