这才是英伟达的真正威胁

Core Viewpoint - NVIDIA's main competitor in the AI hardware race is Google, not AMD or Intel, as highlighted by the recent launch of Google's Ironwood TPU, which significantly enhances its competitive position against NVIDIA [2][10]. Group 1: Ironwood TPU Specifications - Google's Ironwood TPU features 192GB of HBM memory with a peak floating-point performance of 4,614 TFLOPs, representing a nearly 16-fold improvement over TPU v4 [5][4]. - The Ironwood TPU Superpod can contain 9,216 chips, achieving a cumulative performance of approximately 42.5 exaFLOPS [5][4]. - The inter-chip interconnect (ICI) technology allows for a scalable network, connecting 43 modules, each with 64 chips, through a 1.8 PB network [3]. Group 2: Performance Improvements - Compared to TPU v5p, Ironwood's peak performance has increased by 10 times, and it shows a 4-fold improvement over TPU v6e in both training and inference workloads [4][6]. - The architecture of Ironwood is specifically designed for inference, focusing on low latency and high energy efficiency, which is crucial for large-scale data center operations [6][7]. Group 3: Competitive Landscape - The AI competition is shifting from maximizing TFLOPS to achieving lower latency, cost, and power consumption, positioning Google to potentially surpass NVIDIA in the inference market [10]. - Google's Ironwood TPU is expected to be exclusively available through Google Cloud, which may lead to ecosystem lock-in, posing a significant threat to NVIDIA's dominance in AI [10]. Group 4: Industry Insights - The increasing focus on inference queries over training tasks indicates a shift in the AI landscape, making Google's advancements in TPU technology particularly relevant [6][10]. - NVIDIA acknowledges the rise of inference technology and is working on its own solutions, but Google is positioning itself as a formidable competitor in this space [10].