“TensorFlow Research Cloud (TRC): Accelerate Your Cutting-Edge Machine Learning Research With Free Cloud TPUs”, (; backlinks; similar):
The TensorFlow Research Cloud (TRC) program enables researchers to apply for access to a cluster of more than 1,000 Cloud TPUs. In total, this cluster delivers a total of more than 180 petaflops of raw compute power! Researchers accepted into the TRC program can use these Cloud TPUs at no charge to accelerate the next wave of open research breakthroughs. Participants in the TRC program will be expected to share their TRC-supported research with the world through peer-reviewed publications, open source code, blog posts, or other means. They should also be willing to share detailed feedback with Google to help us improve the TRC program and the underlying Cloud TPU platform over time. In addition, participants accept Google’s Terms and Conditions, acknowledge that their information will be used in accordance with our Privacy Policy, and agree to conduct their research in accordance with the Google AI principles. Machine learning researchers around the world have done amazing things with the limited computational resources they currently have available. We’d like to empower researchers from many different backgrounds to think even bigger and tackle exciting new challenges that would be inaccessible otherwise.
[TRC is an easy-to-apply cloud credit program which grants free access to up to hundreds of GCP TPUs (typically ~100 pre-emptible individual TPUv2s and a scattering of TPUv2-8s and TPUv3s as of 2020) and sometimes whole TPU pods to researchers & hobbyists like me; I relied on TRC credits to train a variety of GPT-2-1.5b models which are infeasible on consumer GPUs. It took seconds to apply with an email address, they replied in hours with credits, and were highly responsive thereafter as we encountered various TPU issues. Warning: as of mid-2023, TRC available & resources may be much lower than historically (possibly due to the AI race beginning late 2022), although they claim to still be highly active.]