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Training frontier AI models requires moving massive amounts of data between GPUs without wasting compute. To tackle this, Meta designed MetaRoCE—a custom RDMA transport protocol built natively for AI-scale Ethernet, solving extreme network congestion and reliability issues....

Meta deployed GEM, a generative foundation model for ad recommendations trained across thousands of GPUs. By rethinking their synchronization architecture and applying LLM scaling laws, they overcame massive infrastructure bottlenecks to double their training efficiency....

Meta just introduced MetaRoCE, a custom RDMA transport protocol, alongside its MTIA 300 training chip with built-in NIC chiplets. This combination directly addresses massive networking bottlenecks in scaling frontier AI models by optimizing GPU-to-GPU communication....

Meta just unveiled MetaRoCE, a custom RDMA network transport built to solve massive data bottlenecks in AI-scale Ethernet. By ensuring reliable, high-speed data transfer between GPUs, it prevents expensive compute cycles from being wasted during frontier model training....

Databricks introduced Smart Routing in the Unity AI Gateway, dynamically sending queries to different LLMs based on task complexity. This prevents overpaying for simple tasks, slashing inference costs by over 30% while mathematically matching frontier model output quality....