The performance of TPU v3 compared to TPU v2 is significantly enhanced. A TPU v3 pod achieves eight times the performance of a TPU v2 pod, reaching over 100 petaflops of compute power[1]. This substantial increase in performance allows developers to undertake complex machine learning tasks more efficiently and achieve faster results.
In terms of architecture, TPU v3 incorporates a more efficient cooling system, using water cooling, which reduces the vertical space required and enables more TPUs to be packed into a pod compared to TPU v2[1]. TPU v3 also offers more memory and bandwidth, with each board having 16 GB of memory, compared to 8 GB in TPU v2[5][7].
Overall, the advancements in TPU v3 make it more suitable for large-scale machine learning projects that require high computational power and efficiency.
Citations:[1] https://www.toolify.ai/ai-news/unveiling-the-evolution-of-tpus-tpu-v2-and-tpu-v3-1727331
[2] https://www.reddit.com/r/MachineLearning/comments/zil35t/d_does_google_tpu_v4_compete_with_gpus_in/
[3] https://www.datacamp.com/blog/tpu-vs-gpu-ai
[4] https://massedcompute.com/faq-answers/?question=How+does+the+power+consumption+of+TPU+v3+compare+to+other+Google+TPUs+in+a+data+center+setup%3F
[5] https://www.run.ai/guides/cloud-deep-learning/google-tpu
[6] https://openmetal.io/docs/product-guides/private-cloud/tpu-vs-gpu-pros-and-cons/
[7] https://news.ycombinator.com/item?id=22195516
[8] https://cloud.google.com/tpu/docs/v2
[9] https://eng.snap.com/training-models-with-tpus