TPUv7: Google Takes a Swing at the King
13 days ago
- #Google TPU
- #AI Hardware
- #Nvidia Competition
- Google's TPUv7 is challenging Nvidia's dominance in AI hardware, with major AI labs like Anthropic, Meta, SSI, xAI, and potentially OpenAI adopting TPUs.
- Anthropic's significant investment in TPUs (1 million units) highlights TPU's cost and performance advantages over Nvidia GPUs, with up to 52% lower TCO per effective PFLOP.
- Google's TPUv7 Ironwood offers competitive performance with Nvidia's Blackwell, closing the gap in FLOPs, memory bandwidth, and capacity, while maintaining lower TCO.
- Google's ICI (Inter-Chip Interconnect) architecture enables massive scale-up clusters (up to 9,216 TPUs), providing superior reconfigurability, lower latency, and better locality compared to Nvidia's NVLink.
- Google is improving its TPU software ecosystem, including PyTorch native support and vLLM/SGLang integration, to attract more external developers and compete with Nvidia's CUDA ecosystem.
- Despite advancements, Google's TPU software strategy still lacks open-source XLA:TPU compiler and runtime, which could accelerate adoption and improve debugging for users.
- Nvidia faces growing competition from Google's TPUs, with potential impacts on its market share and margins, especially as more AI labs diversify their hardware investments.
- Google's unique financing model with Neocloud providers (like Fluidstack) and cryptominers (like TeraWulf) is reshaping the AI datacenter market, offering flexible solutions for TPU deployment.