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

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