AI CURATED HOT STORY · 10/15/2026
Apple has released an updated version of the MLX framework, introducing Remote Direct Memory Access (RDMA) over Thunderbolt 5 to enable low-latency, high-speed interconnects and distributed inference across multiple Mac Studio units. This breakthrough allows for the local execution of frontier models with nearly one trillion parameters, significantly lowering deployment barriers.
Cross-verified reports indicate that this solution was tested with Meta's newly open-sourced Muse Spark Open model, which features an 816B total parameter MoE architecture with 42B active parameters, native multimodality, and a 1M token context window. Leveraging Apple Silicon's unified memory architecture, the new MLX framework achieved generation speeds exceeding 36 tokens/s in a four-node cluster and reduced time-to-first-token latency by up to 4.6x compared to previous generations, validating its efficiency for long-range reasoning tasks.
This development marks a major leap in desktop-level AI computing ecosystems. By encapsulating hardware interconnect technologies within software, developers can now process ultra-large models locally without relying on expensive data centers. This not only accelerates the adoption of open-weight models but also provides competitive local inference solutions for privacy-sensitive industries, reshaping the landscape of edge computing combined with large language models.