Anatomy of a high-performance EP kernel
This article provides a technical breakdown of 'wide Expert Parallelism' (wideEP) used in Mixture-of-Experts (MoE) models for large-scale LLM inference. It explains how routing tokens to specific experts across different GPUs creates unique communication challenges compared to standard parallelization methods.
Why it matters
As MoE models become the standard for high-performance AI, understanding the underlying hardware communication bottlenecks is critical for engineers scaling production-grade LLM infrastructure.
Large language models are large. Because they’re large, we need lots of GPUs to run them. It would be nice if LLM inference were ‘embarrassingly parallel’ and we could just always compute independent things on different GPUs. But alas, to use lots of GPUs on LLM inference, we need to get those GPUs talking to one another.
The content is purely technical and focuses on engineering architecture without political or social commentary.
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