Ai2 releases Olmo-core 3 to make developing large mixture-of-experts AI models more efficient

What happened

Seattle-based artificial intelligence research firm Allen Institute for AI announced a development framework for large language model (the kind of AI system trained on text to produce text)s Thursday that significantly improves how mixture-of-experts large language models are trained. Mixture-of-experts models operate differently from dense […] The post Ai2 releases Olmo-core 3 to make developing large mixture-of-experts LLMs more efficient appeared first on SiliconANGLE .

UPDATED 12:15 EDT / OCTOBER 02 2026 AI Ai2 releases Olmo-core 3 to make developing large mixture-of-experts LLMs more efficient by Kyt Dotson Seattle-based artificial intelligence research firm Allen Institute for AI announced a development framework for large language models Thursday that significantly improves how mixture-of-experts large language models are trained. The new framework, Olmo-core 3, allows MoE training to reach the trillion-parameter scale while keeping costs low by preserving computational efficiency.

The new framework, Olmo-core 3 , allows MoE training to reach the trillion-parameter scale while keeping costs low by preserving computational efficiency. Mixture-of-experts models operate differently from dense AI models: they split computation across specialist portions of the model each time a token is generated, whereas dense models activate the entire model.

Equivalently, training an MoE model allows activating only portions of it while learning each token, a small piece of text — often a word or a part of a word — that an AI reads and generates. Using the same infrastructure, LLMs can scale to over one trillion parameters.

Key facts

• Seattle-based artificial intelligence research firm Allen Institute for AI — announced: a development framework for large language models Thursday that significantly improves how mixture-of-experts large language models are trained