Scaling DeepSeek and Sparse MoE models in vLLM with llm-d

This blog post discusses the advancements in serving large-scale Mixture of Experts (MoE) language models using vLLM and the llm-d project. It explores architectural innovations, kernel-level changes, and optimization techniques that enable effective deployments, particularly in Kubernetes environments. Key improvements include data parallel attention, specialized communication patterns, and intelligent inference scheduling, making these technologies suitable for complex LLM applications. Overall, it highlights the evolution of MoE models and their implications for high-performance AI systems.