MAX Serve has introduced Paged Attention and Prefix Caching to optimize LLM inference, now available in MAX nightly builds and Docker images. These features enhance the efficiency of Multi-Head Attention (MHA), which is resource-intensive, by managing memory more efficiently through KV Cache optimization. Paged Attention, developed by vLLM, uses block-based memory management to reduce memory fragmentation and improve GPU memory savings by up to 40%. Prefix Caching, from SGLang, optimizes prompt processing by caching common prefix patterns, offering a throughput improvement of up to 3x for structured workflows. These advancements aim to significantly improve resource utilization and processing speed in LLM applications, and users are encouraged to try these features via the magic CLI and share their experiences on social media.