Granite 4.1 are compact language models from IBM with 3B, 30B and 83B parameters, trained on 15 trillion tokens with a 512K context window. The 8B Instruct model outperforms the larger predecessor model through optimized dense architecture and advanced fine-tuning and reinforcement learning techniques.

Allen Institute introduces EMO – a Mixture-of-Experts model in which modular structures develop naturally from data without predefined specifications. The model, code, and technical documentation are freely available.

Asynchronous batch processing enables parallel CPU and GPU work and can increase LLM inference speed by 24 percent without requiring new kernels or models.