Achieving 4× LLM Inference Performance with KV Cache Optimization

​Join GMI Cloud and Yujing Qian (VP of Engineering) at the DDN theater for a deep dive into how to unlock 4× LLM inference performance through KV cache optimization.

​As real-time AI applications scale, teams face growing challenges around memory bottlenecks, latency, and GPU efficiency. This session breaks down how to rethink your inference stack to achieve faster, more efficient performance in production.

​What You’ll Learn

​• How to optimize memory bottlenecks in LLM inference
• Techniques to reduce latency for real-time AI applications
• Strategies to maximize GPU utilization and ROI
• How KV cache optimization drives scalable performance gains

​Why Attend

​If you're building or scaling LLM-powered applications, this session will show how to move from hardware limits → optimized inference performance.

​⚡ Stop by DDN Booth #1621 and join the session live.