Recently, foreign media reported that ByteDance is accelerating its efforts to self-develop and manufacture AI chips to reduce dependence on expensive chips from NVIDIA. The report claims that ByteDance plans to collaborate with TSMC and expects to mass-produce two self-developed AI chips by 2026, having already reserved the production of hundreds of thousands of chips. More notably, the report pointed out that ByteDance aims for its self-developed chip clusters to achieve four times the computing performance of a single H100 chip at a similar cost. In response, ByteDance quickly addressed this report.

Recently, the foreign media outlet The Information reported that ByteDance is accelerating the development of its own AI chips and plans to collaborate with TSMC, aiming for mass production by 2026. The report also stated that this move by ByteDance is intended to reduce dependence on NVIDIA chips and lower the costs of developing and operating AI models. Source note: The image is generated by AI and provided by the image licensing service Midjourney, according to reports from the Sci-tech Innovation Board Daily regarding today's media coverage of ByteDance's plans to collaborate with TSMC on AI chips.

According to the Financial Times, BlackRock, one of the world's largest asset management companies, will collaborate with tech giant Microsoft and Abu Dhabi-backed investment firm MGX to launch a large AI fund exceeding $30 billion. The fund will primarily be used to build data centers and invest in energy infrastructure to meet the growing demand for AI computing. Additionally, chip giant NVIDIA will provide technical support for the fund to address the challenges posed by AI technology.

Recently, at the launch event for the new EX90 electric SUV, Volvo announced its continued collaboration with NVIDIA, stating that future models will be equipped with NVIDIA's chips. The EX90 is Volvo's first software-defined vehicle, featuring the NVIDIA Orin system-on-chip, which boasts computation capabilities exceeding 250 trillion operations per second. This SUV's core computing architecture not only supports deep learning functions but also provides robust support for its AI-driven active safety and driving assistance systems. Furthermore, Volvo plans for future enhancements.

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.

The current tech industry mainly relies on the boom of artificial intelligence (AI) to mask its overall sluggish condition, with many companies continuing to struggle amid economic downturns. Traditional tech sectors, such as software, IT consulting, and companies providing electronic devices for manufacturing and the automotive industry, face problems of weak demand and excess inventory, making recovery difficult. While AI has become the sole growth point, the aftermath of overhiring and excessive spending, coupled with economic uncertainty, has increased the difficulty of recovery. Large tech companies are experiencing a slowdown in growth, and small businesses in the tech sector are particularly facing weak revenue growth. Although AI is the current highlight,

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.

Tokyo-based AI development company Sakana AI has announced the completion of a $100 million Series A financing round, with investors including NEA, Khosla Ventures, Lux Capital, and NVIDIA. Sakana AI focuses on nature-inspired intelligence and model optimization, particularly in evolutionary optimization and sparsity. The partnership with NVIDIA will provide advanced GPU technology support and access to Japanese data centers, aimed at promoting AI research and innovation. Collaboration goals include advancing research and leveraging NVIDIA's infrastructure.

AMD announced that Keith Strier has been appointed as the Senior Vice President of Global Artificial Intelligence Market, aiming to drive the company's worldwide AI strategy development. Strier's addition will lead AMD in advancing its AI vision globally, enhancing ecosystem capabilities, and accelerating AI collaborations across various industries. As an expert with extensive experience in the field of artificial intelligence, he will report directly to AMD's Executive Vice President and Chief Technology Officer. AMD Chair Lisa Su emphasized that Strier's market expertise and industry network will enhance the company's influence in the global AI arena.

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.

Nvidia is facing an antitrust investigation by the U.S. Department of Justice, focusing on its dominant market position in the artificial intelligence sector and whether it has taken measures to stifle competition, including penalizing suppliers that do not use its AI chips. This news has led to a significant drop in Nvidia's stock price, resulting in a market value loss of $279 billion. Although the overall trend of stock prices remains an upward trajectory, the AI boom has driven high sales of its expensive chips. Nvidia's recently launched Blackwell series AI chips are facing delayed market release due to design flaws, intensifying competition with rivals.

'OmniRe' technology has been jointly developed by several top universities and research institutions, aiming to comprehensively reconstruct urban scenes, including vehicles, pedestrians, and bicycles. Unlike traditional methods that rely on local perspectives, 'OmniRe' integrates multi-angled information to achieve a more accurate capture of urban environments, significantly enhancing reconstruction quality. This groundbreaking advancement holds vital importance for fields such as urban planning, autonomous driving, and augmented reality. 'OmniRe' not only generates high-precision 3D models but also possesses the capability to efficiently process large amounts of data, expediting...

NVIDIA has collaborated with several universities to introduce NVEagle, a large visual language model capable of chatting using images. NVEagle can analyze image content and provide accurate answers, such as identifying individuals in images, like Jensen Huang. The model significantly enhances the understanding of visual information by transforming images into visual tokens and combining them with text embeddings. In addressing the challenges of high-resolution image processing, the research team has constructed models like Eagle-X5-7B and Eagle-X by exploring various visual encoders and fusion strategies.

Nvidia CEO Jensen Huang emphasized in an interview with the Financial Times that despite encountering issues in the production of the next-generation AI processors, the company remains committed to its plan of launching a new flagship product every year. Although the release of the Blackwell chip has been delayed, Nvidia engineers are accelerating the development of the next two generations of chips. The production issues are mainly related to adjustments in the 'mask' process during chip design, and these changes have been completed without affecting product functionality. Nvidia is leveraging AI to enhance data processing and product design efficiency in response to investors' concerns about growth rates and profit margins.

Nvidia is considering joining OpenAI's $100 billion funding round, attracting tech giants including Apple and Microsoft to participate. OpenAI, the developer of ChatGPT, is expected to surpass a $100 billion valuation, thanks to ChatGPT's weekly active user base exceeding 200 million and significant growth in usage. This funding round is led by Thrive Capital, and if Nvidia, Apple, and Microsoft successfully join, it will accelerate OpenAI's technological development and the application of AI across various industries. OpenAI's rise and ChatGPT's...

AI startup Magic has completed a $320 million funding round, with participation from Eric Schmidt, Alphabet's CapitalG, and investors like Atlassian, bringing the total funding close to $500 million. Magic focuses on AI model development aimed at generating code and automating software development tasks, featuring an exceptionally long context window of up to 100 million tokens, surpassing current commercial models. Magic plans to collaborate with Google Cloud to build two supercomputers to enhance

This article outlines the latest developments in the AI inference chip field, particularly the results of the MLPerf Inference v4.1 competition, which gathered the latest chips from companies such as AMD, Google, UntetherAI, Cerebras, FuriosaAI, and Nvidia. Nvidia's H200 and GH200 chips maintain a lead in the 'Data Center Closed' category, but chips from other companies excelled in specific tasks, especially in terms of energy efficiency. AMD's Instinct Accelerator.

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.