Breaking the GPU Memory Wall for AI Inference

As AI models continue to grow in size and context windows expand, GPU memory has become a critical limitation for achieving fast, efficient inference. When context memory capacity is exceeded, organizations experience increased latency, context recomputation, reduced throughput, and inefficient GPU utilization.

Join MinIO for a technical discussion on how MinIO MemKV addresses the growing challenge of inference context memory, with participation from NVIDIA on the underlying infrastructure demands of AI inference.

Learn how MemKV provides a distributed, high-performance context memory layer that extends GPU memory capacity using RDMA-connected, memory-mapped NVMe storage.

Attendees will learn how MinIO MemKV:

• Reduces context eviction and costly recomputation
• Improves token throughput and reduces per-token latency
• Provides low-latency access to distributed context memory
• Uses zero-copy RDMA, native NIXL integration, and parallel extent-based architecture
• Enables scalable AI infrastructure using a shared-nothing architecture