Catch this podcast with @niclane7 of CaMLSys, especially watch out for the discussion of large-scale decentralized training of LLMs pioneered in our lab with the Photon system -- jointly developed with @flwrlabs. buzzsprout.com/2417792/episo…

🎉 Huge congratulations to the teams at CaMLSys and BUPT -- particularly Zhenyan Lu, Dongqi Cai, Zexi Li and all the collaborators -- for winning the Best Poster Award at MobiUK 2025 in Edinburgh!🏆 mobiuk.org/2025/abstract/PS1…

This week, we are delighted to host Max Ryabinin (@m_ryabinin) of @togethercompute who will be speaking at the Cambridge ML Systems Seminar Series. His talk is at 3pm June 18th, LT1 @Cambridge_CL

A huge thank you @SPRIND for hosting the Composite Challenge mentor days recently. Looking forward big decentralized training runs in the coming months, and helping to expand participation in AI more broadly.

Tomorrow join us for the latest edition of the Cambridge ML Systems Seminar Series. We are delighted to be joined by @Rosco_Hunter_ who will present "Building Oranizational Resilience with AI Malfunction Drills". See you tomorrow, May 20th at 3.30pm at FW26 in the Computer Lab.

Photon: A New SOTA for Decentralized LLM pre-training at @MLSysConf 2025. Poster today; talk on Thursday. @lorenzosani97 converting one person at a time to the merits of federated everywhere for everything. Paper: arxiv.org/abs/2411.02908

Thanks to everyone who joined online and in person for the fantastic talk by @Jamie_Shotton from @wayve_ai!

Join us for the next Cambridge ML Systems Seminar: @Jamie_Shotton, Chief Scientist at @wayve_ai who give his talk: "Frontiers in Embodied AI for Autonomous Driving" on Friday May 2nd at the @Cambridge_CL in LT1. See you all then!

Lots of interest in SparsyFed @iclr_conf. Big step forward in practical use of sparsity for federated training. High sparsity w/out heavy hyperparameter tuning arxiv.org/abs/2504.05153 Fantastic team: @AdrianoGuastel1, @lorenzosani97, @Alex__Iacob, Alessio Mora, Paolo Bellavista

Join us for the next Cambridge ML Systems Seminar: Alexander Hägele (@EPFL) on "Learning Rate Schedules, Scaling Laws, and Techniques for Pretraining LLMs" 📍 @Cambridge_CL, FW26 🕒 Tue 08 April, 14:00–15:00 Featuring new results from his recent NeurIPS spotlight.