TPUs vs. GPUs and why Google is positioned to win AI race in the long term
14 days ago
- #Google Cloud
- #Machine Learning
- #AI Hardware
- Google TPU was developed to address the inefficiency of CPUs and GPUs for deep learning tasks, specifically to avoid doubling data center capacity due to AI workloads.
- TPUs use a Systolic Array architecture, reducing memory bottlenecks and improving energy efficiency compared to GPUs.
- Performance improvements from TPUv5p to TPUv7 include a 10x increase in BF16 TFLOPS, doubled memory capacity, and significantly higher memory bandwidth.
- TPUs offer better performance per watt and cost-effectiveness for specific AI tasks, with some use cases showing 1.4x better performance per dollar compared to GPUs.
- The main barrier to wider TPU adoption is the lack of ecosystem support compared to Nvidia's CUDA, though Google is improving compatibility with frameworks like PyTorch.
- Google's control over TPU design and software stack gives it a competitive edge in cloud computing margins, reducing reliance on Nvidia.
- TPUs are central to Google's AI strategy, powering models like Gemini 3 and internal AI services, positioning GCP as a leader in AI infrastructure.
- Google's production of TPUs is scaling rapidly, with significant investments to meet both internal and external demand, though exact numbers are not publicly disclosed.