TPUs (Tensor Processing Units) generally offer better energy efficiency compared to GPUs (Graphics Processing Units) for specific tasks, particularly those involving tensor operations, which are fundamental to deep learning and neural networks. TPUs are designed to deliver high performance while minimizing power consumption, making them ideal for large-scale AI applications in data centers where energy efficiency is critical to reduce operational costs and environmental impact[1][2][3]. For instance, Google's TPUs can provide up to 4 teraflops of performance while consuming only a fraction of the power that GPUs might require for similar tasks[2].
GPUs, while powerful and versatile for a wide range of applications, tend to consume more power due to their general-purpose design, which includes a broader range of functionalities beyond AI tasks, such as graphics rendering and scientific simulations[3][5]. However, GPUs are also focusing on energy efficiency, but they typically require more power than TPUs for equivalent AI tasks[2][3].
The latest advancements in TPUs, such as the TPU v4, further enhance energy efficiency by improving performance per watt significantly compared to previous models[7]. This makes TPUs a cost-effective choice for large-scale AI deployments where energy efficiency is a priority.
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://openmetal.io/docs/product-guides/private-cloud/tpu-vs-gpu-pros-and-cons/
[5] https://telnyx.com/learn-ai/tpu-vs-gpu
[6] https://www.linkedin.com/pulse/understanding-cpu-gpu-dpu-tpu-simplified-guide-mohd-asif-ansari-xf8kc
[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