NVIDIA has built a moat with its GPU and CUDA ecosystem, leading countless companies to willingly pay high hardware costs and margins. This is because, during the technological exploration phase, the stability of computing power supply is far more important than cost-effectiveness. However, as AI applications enter the stage of large-scale commercial use, those tech … Read more
In the ASIC market, some recent statements heard are contradictory. “Among the many ASIC projects globally, 90% will fail,” said NVIDIA CEO Jensen Huang. Huang’s comments on ASIC are not frequent. Therefore, once the related remarks were made, discussions about the growth potential of ASIC surged in the market, with various speculations emerging. Huang stated … Read more
Introduction to AI Chips AI Chips: Also known as artificial intelligence chips, AI accelerators, or compute cards, these modules are specifically designed to handle a large number of computational tasks in AI applications. Classification of AI Chips: Based on design requirements, AI chips are mainly divided into Central Processing Units (CPUs), Graphics Processing Units (GPUs), … Read more
How do CPU, GPU, NPU, and FPGA each excel in deep learning? With the rapid development of artificial intelligence, deep learning has become the mainstream method for AI research and applications. Faced with the demand for parallel processing of massive data, the computational power required by AI continues to rise, not only pursuing higher computational … Read more
In the ASIC market, some recent statements heard are contradictory. “Among the many global ASIC projects, 90% will fail,” said Jensen Huang, CEO of NVIDIA. Huang’s comments on ASIC are not frequent. Therefore, once the related remarks were made, discussions about the growth potential of ASIC surged in the market, with various speculations emerging. Huang … Read more
According to a report by Electronic Enthusiasts Network (Author: Huang Jingjing)Analyst Ben Reitzes from Wall Street research firm Melius Research predicts that Nvidia’s market value could reach $9 trillion by 2030. In July of this year, Nvidia’s valuation reached $4 trillion, based on this calculation the company’s market value will more than double in the … Read more
Aug. Click the blue text to follow us 2025.08 With a positive outlook on the rapid growth of the AI training market, not only is the shipment of AI GPUs from NVIDIA or AMD expected to grow rapidly, but the training ASIC market is also expanding quickly under the push of the four major American … Read more
1. SMIC (Semiconductor Manufacturing International Corporation). It is currently the most advanced and largest wafer foundry in China, supporting domestic chip design needs. 2. Cambricon Technologies. A leader in domestic AI training and inference chips, competing with NVIDIA’s GPU computing platform. 3. Haiguang Information Technology. A leader in domestic X86 CPUs and DCUs, replacing NVIDIA’s … Read more
Remember to star the public account ⭐️ to receive notifications promptly. Source: Content fromBusiness Times. With optimism about the rapid growth of the AI training market, not only is the shipment of AI GPUs from NVIDIA or AMD expected to grow rapidly, but under the push of the four major US CSPs, the market for … Read more
First, here is the table of contents for the chip learning series. 1. Basic Concepts Basic Classification of Chips: CPU, GPU, FPGA, ASIC, memory chips, analog chips, power chips, etc., their roles and differences in the industry. Design vs Manufacturing: Differences between Fabless, Foundry, and IDM models. Key Process Indicators: Process (nm), yield, power consumption, … Read more