As of November 2026, the artificial intelligence landscape continues to see rapid architectural scaling and deployment optimization. Reported releases from early November showcase significant advancements in Mixture-of-Experts (MoE) models and inference acceleration frameworks.
Scaling via Mixture-of-Experts
Recent announcements emphasize the deployment of high-parameter MoE architectures designed to balance immense model capacity with constrained active parameter footprints. Notably, Thinking Machines released Inkling 2 Small, featuring a 594B total parameter count with 17B active parameters. Concurrently, Baidu introduced ERNIE 5.5 Flash Base, a 280B MoE base model operating with 14B active parameters.
Inference Performance and Optimization
Beyond model scaling, infrastructure and compiler technologies have taken center stage. Reported developments include Together AI's Together Compiler, which leverages agents to make inference up to 2.6x faster, and Zyphra's ZYFA, which introduces FP8 FlashAttention for the AMD MI355X to achieve speeds up to 1.76x faster.
*Analysis:* Based on these published reports, the industry trend heavily favors maximizing hardware efficiency through targeted compilation and sparse parameter activation, allowing large architectures to maintain practical inference latencies.