#LLM Serving

Articles about LLM Serving — exploring patterns, best practices, and real-world implementations in production systems.

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Pratik Dhanave · ·6 min read

Training and Serving MoE Models

Mixture of Experts saves compute but not memory — and that single fact reshapes everything about how these models are trained and served. All the experts must exist in memory even though only a few run per token, which turns MoE into a distributed-systems problem as much as a machine-learning one. This post covers the trade MoE actually makes and the parallelism it forces.

Mixture of Experts saves compute but not memory — and that single fact reshapes everything about how these models are trained and served. All the experts must exist in memory even though only a few run per token, which turns MoE into a distributed-systems problem as much as a machine-learning one. The trade MoE makes and the expert parallelism it forces.

#LLMs#Mixture of Experts#LLM Serving#Distributed Systems#AI Architecture#Machine Learning

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.