Efficiency

Microsoft unveils MAI-Base-2 MoE with 2.8T total, 80B active parameters: Microsoft unveiled MAI-Base-2, a mixture-of-experts model with 2.8T total and 80B active parameters. It claims 4x-12x more pretraining FLOPs-efficiency than DeepSeek-V4-Pro-Base. Microsoft also cited MAI-Thinking-1, a general-purpose foundation model post-trained on MAI-Base-1 announced in June.

AI Models • Techmeme

Study finds LLM reasoning uses 3.7× more inference energy on average: A study measured inference energy for 86 open-weight LLM checkpoints across seven benchmarks and six hardware platforms. Most configurations used a median 2.6–4.8× more energy than another configuration scoring at least as well, with gaps up to 27×; reasoning raised energy about 3.7× on average.

AI Research • arXiv