Google DeepMind has released a new textbook titled 'How to Scale Your Model,' aimed at providing insights into the systems engineering of scaling large language models (LLMs) on Tensor Processing Units (TPUs). The textbook is authored by a team of researchers including @_sholtodouglas, @charliexychen, @pchoy95, @albertwebson, @vinayramasesh, and @froystig, and is intended for LLM developers seeking to enhance their understanding of efficient LLM operation. In addition to this release, a new multi-turn evaluation benchmark called MultiChallenge has been introduced, with the leaderboard featuring top-performing models: o1, Claude 3.5 Sonnet, and Gemini 2.0 Pro Experimental, which scored under 50% accuracy, with the highest at 44.93%. This benchmark assesses various aspects of LLM performance, including instruction retention and inference memory. The developments highlight ongoing efforts to improve LLM capabilities and address existing gaps in their reasoning and performance metrics.