IBM is already thriving in the artificial intelligence (AI) industry, and Intel could be a major player down the road.The artificial intelligence (AI) industry is evolving rapidly. AI is getting smarter, although some cracks in the growth story have emerged. OpenAI's highly anticipated GPT-5 model has fallen well short of inflated expectations, which likely throws some cold water on the idea that AI "superintelligence" is right around the corner.While uncertainty is running high, International Business Mach ...

Core Viewpoint - Intel is scaling back its efforts in the AI accelerator market, particularly in AI training, as it acknowledges the dominance of Nvidia and shifts focus towards AI inference and emerging opportunities in agentic AI [1][2][6][11] AI Training Market - Intel has abandoned its Gaudi line of AI chips due to immature software and an unfamiliar architecture, leading to the cancellation of Falcon Shores, which was intended to succeed Gaudi 3 [1] - CEO Lip-Bu Tan stated that it is "too late" for Intel to catch up in the AI training market, recognizing Nvidia's strong market position [2][11] AI Inference Market - AI inference, which utilizes trained models, is seen as a potentially larger market than AI training, with companies like Cloudflare predicting its growth [6] - Intel plans to focus on AI inference and agentic AI, which are emerging areas with significant potential [7][11] Market Opportunities - There is a growing trend towards smaller, more efficient AI models that can run on less expensive hardware, presenting a market opportunity for Intel [9] - Intel could still succeed in AI chips for edge data centers and devices designed to run fully trained AI models [8] Rack-Scale AI Solutions - It remains uncertain whether Intel will continue developing rack-scale AI solutions, as the future of Jaguar Shores is unclear following Tan's statements [10]

3月28日消息，据Barron's报道，英特尔新任CEO陈立武（Lip-Bu Tan）在当地时间3月27日提交的年报中表 示，"我们无疑须开发具竞争力的机柜级系统解决方案，借此强化云端AI数据中心的市场地位，这将是我跟团队的 优先要务。" 目前，英伟达在AI数据中心市场居于霸主地位，占据接近90%的AI芯片市场份额。即便是AMD在2024年也实现了 超过50亿美元的AMD Instinct加速器收入。相比之下，英特尔在AI市场的表现却欠佳。2024年10月，英特尔公司 就曾坦承旗下Gaudi系列AI加速器无法达成之前设定的2024年5亿美元营收目标。今年1月，英特尔新一代AI数据 中心产品"Falcon Shores"被传难产，将转而研发另一款AI数据中心解决方案"Jaguar Shores"。 报道称，陈立武瞄准的竞争产品，是英伟达目前最顶级的GB200 NVL72 Blackwell AI系统。一名英特尔前高层曾 表示，英伟达这套系统是AI计算领域的"终极掠食者"（apex predator）。GB200 NVL72在一台服务器机柜内部连 接了72颗GPU，远多于上一代的8颗GPU，能在有限空间内提供前所 ...

Group 1: Intel's Position in the AI Accelerator Market - Intel has struggled to enter the AI accelerator market, which is currently dominated by Nvidia, and has missed its own sales estimates for AI chips in 2024 [1] - The company has canceled its Falcon Shores product and is now focusing on rack-scale AI solutions that are not expected to be ready until 2026 [1] - Intel's overall opportunity in the AI sector is diminished due to its failure to successfully launch the Gaudi AI accelerators [10] Group 2: CPU Business and AI Applications - As the AI industry matures, Intel's Xeon server CPUs may benefit from the shift in workloads from training to running AI models [2] - The introduction of the Xeon 6 family of server CPUs aims to cover lower price points and specialized use cases, offering up to 68% lower cost of ownership compared to five-year-old systems [4] - Intel's Xeon 6 CPUs deliver up to 50% greater AI inference performance compared to AMD's latest server CPUs [4] Group 3: Market Trends and Future Opportunities - The AI market is projected to see total annual spending on machine learning and analytics reach $361 billion by 2027, with $153 billion specifically for generative AI [9] - As AI models become more efficient and cheaper to run, a growing share of spending may shift towards infrastructure that does not require high-end AI accelerators [9] - Smaller, less capable AI models can be effectively run on CPUs with built-in AI acceleration, making Intel's CPUs a viable option for many AI applications [3][6]

Core Viewpoint - Nvidia's stock price surge, which once made it the highest-valued company globally, has stagnated as investors become cautious about further investments, recognizing that the adoption of AI computing will not be a straightforward path and will not solely depend on Nvidia's technology [1]. Group 1: Nvidia's Growth Factors and Challenges - Nvidia's most profitable product is the Hopper H100, an enhanced version of its graphics processing unit (GPU), which is set to be replaced by the Blackwell series [3]. - The Blackwell design is reported to be 2.5 times more effective in training AI compared to Hopper, featuring a high number of transistors that cannot be produced as a single unit using traditional methods [4]. - Nvidia has historically invested in the market since its founding in 1993, betting on the capability of its chips to be valuable beyond gaming applications [3][4]. Group 2: Nvidia's Market Position - Nvidia currently controls approximately 90% of the data center GPU market, with competitors like Amazon, Google Cloud, and Microsoft attempting to develop their own chips [7]. - Despite efforts from competitors, such as AMD and Intel, to develop their own chips, these attempts have not significantly weakened Nvidia's dominance [8]. - AMD's new chip is expected to improve sales by 35 times compared to its previous generation, but Nvidia's annual sales in this category exceed $100 billion, highlighting its market strength [12]. Group 3: AI Chip Demand and Future Outlook - Nvidia's CEO has indicated that the company's order volume exceeds its production capacity, with major companies like Microsoft, Amazon, Meta, and Google planning to invest billions in AI and AI-supporting data centers [10]. - Concerns have arisen regarding the sustainability of the AI data center boom, with reports suggesting that Microsoft has canceled some data center capacity leases, raising questions about whether it has overestimated its AI computing needs [10]. - Nvidia's chips are expected to remain crucial even as AI model construction methods evolve, as they require substantial Nvidia GPUs and high-performance networks [12]. Group 4: Competitive Landscape - Intel has struggled to gain traction in the cloud-based AI data center market, with its Falcon Shores chip failing to receive positive feedback from potential customers [13]. - Nvidia's competitive advantage lies not only in hardware performance but also in its CUDA programming language, which allows for efficient programming of GPUs for AI applications [13].