PagedWeight: Efficient MoE LLM Serving with Dynamic Quality-Aware Weight Quantization
Mixture-of-Experts (Mixture-of-Experts AI) is a popular class of large language models (LLMs), offering high efficiency and accuracy. However, in KV-cache-intensive serving scenarios, MoEs often exhibit a tension between the GPU memory requirements of the model weights and the growing KV cache. We propose PagedWeight, a novel management method for Mixture-of-Experts AI LLM serv
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Mixture-of-Experts (Mixture-of-Experts AI) is a popular class of large language models (LLMs), offering high efficiency and accuracy. However, in KV-cache-intensive serving scenarios, MoEs often exhibit a tension between the GPU memory requirements of the model weights and the growing KV cache. We propose PagedWeight, a novel management method for Mixture-of-Experts AI LLM serv
This infrastructure update advances Quantization and MoE, directly affecting how engineers build, optimize, and ship AI systems. Teams running production LLM workloads need to understand these techniques to stay competitive in hiring and architecture decisions.