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Tensordyne introduces Napier, an inference system using logarithmic math on silicon, claiming massive efficiency gains for MoE and reasoning models, with air-cooled racks.
Analysis of the trend in AI model sizes, noting a gap in the 100-120B parameter range with recent releases focusing on smaller (25-35B) or larger (200B+) models.
User benchmarks show no significant speed difference between Windows 11 and Linux when running large MoE models with llama.cpp, debunking a common myth. Tests on a multi-GPU setup with models like Qwen 3.5 122B, 397B, and MiniMax 2.7 yield nearly identical prompt processing and token generation speeds.
Apple Silicon Macs offer large memory pools for running big models but with slower token generation, performing best with large MoEs that have low active parameters.