Hyperscalers are more frequently developing their own AI custom chips.

Arista Networks is a titan in data center networking, second only to Nvidia

Private equity firms are investing in AI infrastructure - here's how.

Learn how the Cisco and NVIDIA partnership empowers on-premises AI solution

Meta Platforms Inc, the company behind Facebook and Instagram, has recently

Google Cloud Next '24 was an important event for Google - here's what's new

Microsoft to invest US$2.9 billion in AI and cloud infrastructure in Japan

Microsoft and OpenAI are setting a new benchmark in the field of AI.

The recent NVIDIA GTC 2024 event has sparked interest and discussion in the tech community, giving us a glimpse into the future of artificial intelligence (AI) and high-performance computing (HPC). Let's review some of the most important takeaways from the conference and their implications for AI infrastructure.

Computing has undergone a remarkable journey since the 1960s, with each new era bringing forth groundbreaking innovations that have reshaped the technological landscape. This article delves into the major computing cycles, from the era of IBM's System/360 to the rise of NVIDIA, CUDA, and the generative AI age. We will explore the infrastructure and computing power aspects that have driven these transformations, focusing on the technical advancements that have enabled the AI revolution and the emergence of vector databases.

In a stunning display of investor enthusiasm for artificial intelligence (AI), autonomous vehicle software company Applied Intuition has secured $250 million in a Series E funding round, propelling its valuation to an impressive $6 billion. The round was led by Lux Capital's Bilal Zuberi, investor Elad Gil, and Porsche Investments Management, with participation from Andreessen Horowitz, Bond, and Formula 1 world champion Nico Rosberg.

The graphics processing unit (GPU) has been through remarkable progress over the past few decades. Originally designed primarily for gaming and graphics, GPUs now play a pivotal role in artificial intelligence (AI) and deep learning. Their highly parallel architecture makes them uniquely suited to handle the computationally intensive workloads of neural networks.

Microsoft's recent announcement of a €3.3 billion investment in Germany for AI and data centers has sent ripples through the tech industry. This substantial commitment signifies a strategic shift, highlighting the growing importance of robust data center infrastructure for fostering AI development and deployment. This article delves deeper into this investment, analyzing its implications for Europe's AI landscape and the broader global context.

The quest for artificial intelligence (AI) that emulates human-like capabilities has long been a driving force in technological innovation. While traditional AI has achieved new feats in areas like machine learning and deep learning, it often lacks the efficiency and adaptability of the biological brain. This is where neuromorphic computing might change our speed of progress, giving us a novel approach to AI hardware inspired by the very structure and function of the human brain.

Sam Altman, the CEO of OpenAI, has sent shockwaves through the tech world with his audacious proposal: raising a staggering $7 trillion to bolster global AI infrastructure. This ambitious plan, met with a mix of intrigue and skepticism, has ignited a crucial conversation about the future of artificial intelligence and the infrastructure needed to support its burgeoning potential. To fully comprehend the significance of Altman's proposition, we must look at the factors at play in this industry and how AI infrastructure investments will be one of the most interesting areas to watch in the coming years.

Retrieval augmented generation (RAG) is a shift in how artificial intelligence is managed and delivered, combining the strengths of retrieval-based systems with powerful generative models. As computing moves beyond a pure retrieval model to incorporate generative capabilities, RAG offers immense potential to transform how AI systems understand, interpret, and synthesize information. This article provides a technical overview of RAG, examining its components, workflow, benefits, and limitations.