What if the biggest bottleneck in AI training wasn't compute, but communication? mKernel eliminates it.
Executive Summary: UC Berkeley's mKernel fuses compute and communication into a single GPU kernel, slashing overhead by up to 47% and threatening NCCL's dominance.
Intro: The core shift from host-driven to GPU-driven communicationAnalysis: Strategic consequences for NVIDIA, AWS, and AI labsBottom Line: Why mKernel changes the economics of large-scale training
Strategic Impact: Communication overhead consumes up to 47% of training time in MoE models. mKernel eliminates this waste by fusing compute and communication into a single GPU kernel. Organizations that adopt mKernel can slash training costs and time-to-market, while those that ignore it will fall behind. The shift from host-driven to GPU-driven communication is inevitable—act now or lose the edge.
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