Read below on how our teams compressed months of low-level RDMA networking and custom TPU kernel development into hours ⬇️
Google's graph teams use Gemini 4 Argon to speed up graph algorithms and TPU kernels
AIGoogle's Graph Mining and Graph Learning teams used Gemini 4 Argon with the Teamwork agent in Google Antigravity to compress months of RDMA networking and custom TPU kernel work into hours. The post says the FarArray graph algorithms run up to 180x faster using 22x less RAM. It also reports custom TPU kernels for Graph Neural Networks running 8–15x faster than native JAX.
@GoogleResearch
Today, we shared how Google’s Graph Mining and Graph Learning teams used Gemini 4 Argon with Teamwork agent in @GoogleAntigravity to solve a different class of internal engineering bottleneck: compressing months of low-level RDMA networking and custom TPU kernel development into hours. Specifically, improvements include accelerated graph algorithms with FarArray (up to 180x faster, 22x less RAM) and custom TPU kernels for Graph Neural Networks that run 8–15x faster than native JAX with massive HBM savings. For me, this update is a full-circle experience: Graph Mining contributed deeply to Gemini Data via deduping and data curation over time, and now Gemini is giving back by scaling and improving Graph Mining library! This is also a close collaboration amongst our Graph Mining and Teamwork teams, together with the Antigravity and Gemini teams. Congratulations! Read the full blog: https://antigravity.google/blog/gemini-4-argon-trillion-edge-graphs-and-tpu-kernelsView quoted post on X
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Google Research · x.comPublished
Original title: Read below on how our teams compressed months of low-level RDMA networking and custom TPU kernel development into hours ⬇️