Feeding the Future of AI | James Coomer

Inference Market & KV Cache Importance - Inference spending is projected to surpass training spending, highlighting its growing significance in the AI landscape [2] - KV cache is crucial for understanding context in prefill stages and augmenting tokens in decode stages during inference [3][4] - Utilizing DDN as a KV cache can potentially save hundreds of millions of dollars by retrieving previously computed contexts instead of recomputing them [5] Disaggregated Inference & Performance - Disaggregated inference, running prefill and decode on different GPUs, improves efficiency, requiring a global KV cache for information dissemination [6] - DDN's fast storage delivers KV caches at extremely high speeds, leading to massive efficiency gains [9] - DDN's throughput is reportedly 15 times faster than competitors, resulting in a 20 times faster token output [10] Productivity & Cost Efficiency - Implementing a fast shared KV cache like DDN can lead to a 60% increase in output from GPU infrastructure [12] - DDN aims to deliver a 60% increase in tokens output per watt, per data center, per GPU, and per capital dollar expenditure [13] - Using DDN offers the strongest improvement in GPU productivity over the next five years by accelerating inference models [12]