SemiAnalysis says its first third-party TPUv7 Ironwood inference benchmarks show up to ~50% better performance/$ vs. B200/B300 on FP8 aggregated serving, with the advantage driven by lower TCO rather than superior raw chip performance.
Google is externalizing the TPU software stack, with native TorchTPU support for vLLM/SGLang targeted to open-source around mid-October, materially lowering the CUDA/software-moat barrier for outside customers.
SemiAnalysis expects the performance gaps with NVIDIA to narrow as Google adds disaggregation/spec decoding, and TPUv8i adds native FP4 and an inference-specific network architecture aimed directly at Rubin-era workloads.
Cloud CEO Thomas Kurian stated that Google Cloud is acquiring new customers at twice the rate of a year ago, with $100m-plus deals doubling, indicating strong growth and market penetration.
The company has 17 product lines each exceeding $1bn in revenue, demonstrating a diversified and successful product portfolio.
Customers typically spend 50% more than they commit, suggesting strong customer satisfaction and expansion within the platform.
The TPU business is more than twice the size of the next largest hyperscaler’s, highlighting a significant lead in AI-specific hardware.
AI-server payback is under two years, and half that on Google’s own chips, indicating efficient and cost-effective AI infrastructure.
The majority of infrastructure contract value is in five-year contracts, providing long-term revenue visibility.
TPUs are now sold in three ways, including outright purchase for customers’ own data centers and a neocloud setup with Blackstone, expanding market access.
Gemini Enterprise is used by over 90% of the Fortune 100, and AI attach across Cloud customers is 80%, demonstrating strong enterprise adoption of AI solutions.