Recently, NVIDIA's newly launched Blackwell AI chip has encountered overheating issues in servers, raising concerns among customers about their ability to timely activate new data centers. According to The Information, the Blackwell graphics processing units (GPUs) are overheating when connected to server racks designed to accommodate 72 chips. Insiders reveal that NVIDIA's engineering team is actively addressing this issue, and the company's staff have already begun...

Recently, NVIDIA unveiled its brand-new Blackwell platform and showcased its initial performance in the MLPerf Training 4.1 benchmark tests. According to the test results, Blackwell achieved a doubling of performance in certain aspects compared to the previous Hopper platform, garnering widespread attention in the industry. In the MLPerf Training 4.1 benchmark tests, the Blackwell platform excels in LLM (Large Language Models).

American semiconductor company AMD has recently announced a global workforce reduction of about 4%, with the number of layoffs expected to be under 1,000. This move comes as AMD continues to surpass Intel in the x86 processor market but faces fierce competition from industry giant Nvidia in its transition from graphics processing units (GPU) to artificial intelligence (AI) accelerators. An AMD spokesperson stated, 'To align our resources with our biggest growth opportunities, we are taking some targeted steps.'

Against the backdrop of growing global demand for advanced artificial intelligence infrastructure, Silicon Valley's GPU cloud computing provider GMI Cloud announced the completion of $82 million in Series A funding. The funding was led by Headline Asia and supported by notable partners such as Banpu Next and Wistron. Following this round of funding, GMI Cloud's total capital has exceeded $93 million, with the funds aimed at opening a new data center in Colorado.

Recently, NVIDIA's latest generation Blackwell GPU has sparked unprecedented demand, with CEO Jensen Huang revealing in an investor meeting that the supply for the next 12 months is completely sold out. This phenomenon is similar to the explosive demand for the Hopper GPU a few months ago, indicating a strong market desire for high-performance computing. This surge in sales is primarily driven by traditional clients, including AWS, Google, Meta, Microsoft, Oracle, and CoreWeave.

As artificial intelligence increasingly integrates into enterprise workflows and products, the demand for Machine Learning Operations (MLOps) platforms is rising. These platforms assist companies in creating, testing, and deploying machine learning models more easily. However, despite the presence of several competitors in the market, such as startups like InfuseAIComet and major companies like Google Cloud, Azure, and AWS, South Korea's VESSL AI aims to carve out its niche by focusing on optimizing GPU costs.

Oracle has announced the launch of the world's first Zettascale cloud computing cluster. This cluster is equipped with up to 131,072 NVIDIA Blackwell GPUs, capable of delivering a peak performance of up to 2.4 ZettaFLOPS. This powerful AI infrastructure enables enterprises to process large-scale AI workloads with greater flexibility and sovereignty. Oracle Cloud Infrastructure (OCI) integrates with NVIDIA's advanced GPU architecture.

With the rapid development of the global artificial intelligence market, Graphics Processing Units (GPUs) are essential as a core driving force for applications driven by large language models. In the coming years, GPU price volatility is expected to increase significantly, affecting companies' cost management. Traditionally, energy-intensive industries such as mining and logistics have been accustomed to cost fluctuations, while sectors like finance and pharmaceuticals need to adapt quickly. Nvidia, as a key supplier, has seen a surge in GPU demand and soaring valuations. The popularity of GPUs stems from their ability to handle vast amounts of computations in parallel, making them suitable for training and deploying large.

The rapid development of AI technology has propelled GPUs to become a hot commodity worldwide, with Nvidia reporting significant growth in the second quarter, exceeding $30 billion in revenue. However, powerful GPUs require adequate data centers to support the AI demand. Nvidia, along with partners, is investing $160 million in Applied Digital to expedite the construction of data centers in North Dakota, aiming to provide data center and cloud services around Nvidia's GPUs. The construction of data centers is crucial, as GPUs are expensive.

Elon Musk's xAI has announced the launch of an artificial intelligence training system named 'Colossus', powered by 100,000 Nvidia H100 GPUs, which is considered the most powerful AI training system globally. Its performance may even surpass that of the U.S. Department of Energy's Aurora supercomputer. The power of Colossus is largely attributed to Nvidia's H100 GPU, which operates language models at a speed 30 times faster than previous GPUs, especially in transformer-based tasks.

Elon Musk showcased the internal structure of Tesla's Cortex AI supercomputer cluster being built at the Austin headquarters, which is expected to require 130 megawatts of cooling and power upon launch, growing to 500 megawatts by 2026. The cluster contains 70,000 AI servers, primarily used to solve real-world AI issues, particularly for training Tesla's full self-driving system and the AI for the Optimus robot. Cortex will rely entirely on Nvidia's hardware at launch, while Tesla's own hardware is expected.

Geekbench AI is a new cross-platform tool designed to evaluate the performance of devices under compute-intensive workloads, with a particular focus on machine learning tasks. It provides full precision, half precision, and quantized scores by measuring the performance of CPUs, GPUs, and NPUs, and includes accuracy assessments. It supports various machine learning frameworks, including ONNX, CoreML, TensorFlow Lite, and OpenVINO. This tool is available on Windows, macOS, Linux, Android, and iOS.

The Transformer architecture, a star in the field of artificial intelligence, has led a revolution in natural language processing with its self-attention mechanism at its core. However, when handling long contexts, the resource consumption of self-attention calculations becomes a bottleneck. To address this issue, researchers have proposed the Tree Attention method, which decomposes the calculation tasks through tree reduction to improve efficiency. This method not only reduces communication overhead and memory usage but is also 8 times faster than existing methods in a multi-GPU environment. The introduction of Tree Attention not only enhances performance.

Google Cloud has collaborated with Y Combinator to offer AI startups $350,000 worth of cloud computing resources, including dedicated GPU/TPU clusters, for a period of two years. This initiative aims to foster AI innovation by prioritizing access to computing resources and facilitating one-on-one discussions with AI experts, helping startups tackle technical challenges and promote their growth. The partnership seeks to nurture future tech giants, with Google aiming to establish long-term relationships through early support. This collaboration not only addresses the computing resource needs of startups.