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A user reports achieving 60 tokens per second with the Ornith-1.5-35B-A3B MOE model on an NVIDIA RTX 4070 Ti, demonstrating efficient local inference optimization.
Recent ternary 1.58-bit LLM releases from small labs demonstrate speed and medical specialization but struggle with long-horizon tasks, with optimism for future models to compete with larger architectures like Qwen.
Using NVIDIA's Nemotron 3 Super and Moonshot AI's Kimi K3 as examples, this article analyzes how the LatentMoE architecture overcomes the efficiency bottleneck of traditional MoE by compressing the Expert computation dimension, and points out that this is a turning point for the next generation of MoE architectures.
The article discusses how the Qwen3.6-35B-A3B model exhibits different failure modes when used as a sub-agent under an orchestrator compared to solo use, particularly due to its MoE architecture and the lack of validation layers, leading to undetected errors.