Google Ironwood TPU Launch: Competitive Challenge to NVIDIA in AI Chip Market

This analysis is based on the Seeking Alpha report [1] published on November 6, 2025, regarding Google’s announcement of its seventh-generation Tensor Processing Unit (TPU), codenamed “Ironwood,” which will become generally available in the coming weeks.

Google’s Ironwood TPU represents a significant technological advancement in the AI infrastructure landscape, delivering 10X peak performance improvement over TPU v5p and more than 4X better performance per chip for both training and inference workloads compared to TPU v6e (Trillium) [5]. The announcement triggered immediate market reactions, with Google (GOOG) trading up +0.44% to $285.99, while NVIDIA (NVDA) declined -3.11% to $189.14, reflecting competitive pressure on the AI chip market leader [0].

The technical specifications of Ironwood are particularly impressive, featuring 4,614 FP8 TFLOPS per chip with 192 GB HBM3E memory, and scalable pods that can reach up to 9,216 AI accelerators delivering 42.5 FP8 ExaFLOPS with 1.77 PB total HBM3E memory [5]. This system-level approach exceeds NVIDIA’s GB300 NVL72 capabilities and demonstrates Google’s commitment to vertical integration in AI infrastructure.

: Google’s dual role as both a major NVIDIA customer and competitor provides unique market insights and could disrupt traditional AI infrastructure models [2]. The company’s system-level co-design approach, combining custom silicon with cloud services, may provide sustainable competitive advantages.

: Anthropic’s commitment to use up to 1 million Ironwood TPUs serves as strong validation of the technology’s enterprise appeal and commercial viability [1][5]. This partnership could accelerate broader market adoption beyond Google’s internal workloads.

: The announcement accelerates the trend of hyperscalers developing custom silicon to reduce dependence on single suppliers [3]. The focus on “age of inference” workloads suggests shifting market dynamics from training to deployment scenarios, potentially reshaping revenue models across the AI infrastructure sector.

: Successful deployment and scaling of Ironwood is critical for Google’s credibility in the AI chip market. Any delays or performance issues could significantly impact market perception [0].

: Enterprise customers may be hesitant to switch from established NVIDIA ecosystems, particularly given CUDA’s entrenched software advantages and developer familiarity [0].

: Google has not disclosed Ironwood pricing, making direct cost comparisons with NVIDIA’s offerings difficult and creating uncertainty for potential customers [1].

: Ironwood’s superior performance-per-watt and system-level capabilities could attract customers seeking to optimize AI infrastructure costs and efficiency [5].

: Google’s ability to control the entire stack from silicon to cloud services may enable unique optimizations and cost structures that competitors cannot easily replicate [2].

: The AI chip market’s rapid disruption creates opportunities for multiple players to succeed, particularly as workloads diversify beyond training to include inference and edge computing [3].

: Google maintains a more reasonable valuation (P/E 28.20x) compared to NVIDIA’s premium (P/E 53.89x), with strong cloud segment performance of $43.23B in FY2024 [0]. NVIDIA faces higher concentration risk with 88.3% of revenue from the Data Center segment ($115.19B in FY2025).

: Ironwood features proprietary 9.6 Tb/s Inter-Chip Interconnect network and superior memory bandwidth compared to current NVIDIA offerings [5]. The system can scale to massive configurations suitable for large-scale AI training and inference workloads.

: Analyst consensus shows 84.8% Buy ratings for GOOG with $330 target price, versus 73.4% Buy ratings for NVDA with $235 target price [0]. This suggests potential upside for Google if Ironwood gains market traction.

: Key factors to watch include official Ironwood pricing announcements, enterprise adoption beyond Anthropic, NVIDIA’s competitive responses, and independent performance benchmarks comparing Ironwood against NVIDIA’s latest offerings [1][5].