AI-native organizations encounter significant scaling issues due to the increasing complexities of agentic AI workflows, where models may reach trillions of parameters and process millions of tokens. Central to overcoming these challenges is the effective management of long-term memory via an efficient Key-Value (KV) cache system. The NVIDIA Rubin platform addresses these needs by integrating the NVIDIA Inference Context Memory Storage (ICMS) platform, designed for gigascale inference. This system provides a specialized storage tier, optimizing KV cache reuse and improving bandwidth efficiency, ultimately enhancing tokens-per-second (TPS) performance by up to five times.
Employing the NVIDIA BlueField-4 data processor and Spectrum-X Ethernet, the ICMS effectively bridges high-speed GPU memory and shared storage, ensuring low-latency access across inference nodes. By transforming KV cache into a vital AI-native resource, organizations can improve total cost of ownership (TCO), reduce power consumption, and enhance overall AI capabilities. This innovative approach positions organizations to smoothly scale in the evolving AI landscape.
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