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White Paper: KV Cache Offload to Improve AI Inferencing Cost and Performance
This paper explores a disaggregated key-value (KV) storage architecture designed to efficiently offload KV cache tensors for generative AI workloads.
With the ever-growing demand for faster and more reliable transmissions, data centers need to be consistently aware of different architectures and applications such as the 5G network architecture, edge computing advancement, etc.
In this whitepaper, we will be providing ideas, design and an actual system created for edge computing, 5G network and AI inference. These aim to solve problems such as drastic environmental conditions, different application areas, inflexible design.
Based on this, we introduce ES200, a system with modularized design that can adapt to different applications and edge computing, and, with those, the accompanying design ideas for data centers.
Leave your contact information to download the whitepaper!
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This paper explores a disaggregated key-value (KV) storage architecture designed to efficiently offload KV cache tensors for generative AI workloads.
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