TPUs (Tensor Processing Units) generally outperform GPUs (Graphics Processing Units) in terms of energy efficiency. TPUs are specifically designed to optimize energy consumption while delivering high performance, particularly in tasks involving tensor operations, which are fundamental to neural networks[1][2][3]. This specialization allows TPUs to achieve higher performance per watt compared to GPUs, which are more general-purpose and consume more power, especially when operating at maximum capacity[3][5].
Google's TPUs, for instance, are engineered to provide high performance while minimizing power consumption, making them ideal for large-scale deployments in data centers where energy efficiency is critical[3][7]. The latest TPU v4 model further enhances this efficiency by improving performance per watt by 2.7 times compared to its predecessor, the TPU v3[7]. In contrast, GPUs, while powerful and versatile, tend to consume more energy due to their broader application scope beyond AI tasks[2][3].
Overall, TPUs offer significant advantages in energy efficiency, making them a preferred choice for specific AI workloads where power consumption is a concern. However, GPUs remain valuable for their versatility and adaptability across a wide range of applications beyond AI[5][6].
Citations:[1] https://www.datacamp.com/blog/tpu-vs-gpu-ai
[2] https://www.wevolver.com/article/tpu-vs-gpu-in-ai-a-comprehensive-guide-to-their-roles-and-impact-on-artificial-intelligence
[3] https://blog.purestorage.com/purely-educational/tpus-vs-gpus-whats-the-difference/
[4] https://arxiv.org/pdf/2309.08918.pdf
[5] https://telnyx.com/learn-ai/tpu-vs-gpu
[6] https://openmetal.io/docs/product-guides/private-cloud/tpu-vs-gpu-pros-and-cons/
[7] https://cloud.google.com/blog/topics/systems/tpu-v4-enables-performance-energy-and-co2e-efficiency-gains
[8] https://www.researchgate.net/publication/342867217_Comparative_Study_on_CPU_GPU_and_TPU