YanRong dramatically enhances AI inferencing performance with KVCache

China's storage software supplier YanRong has integrated KVCache into its YRCloudFile distributed shared file system, significantly enhancing the performance of AI inferencing capabilities. This integration was aimed at improving KV cache hit rates and long-context processing, thereby reducing the costs associated with AI inferencing. The YRCloudFile system is designed for high-performance computing (HPC) and AI workloads and is compatible with all-flash drives and Nvidia's GPUDirect protocol. In a series of tests using public datasets and industry-standard benchmarking tools, YanRong demonstrated that its KVCache achieves substantial improvements in throughput for concurrent queries while lowering the Time to First Token (TTFT) significantly under various loads. The tests involved different configurations and token lengths, with one scenario showing a remarkable 3x to over 13x performance enhancement in TTFT as context length increased. The ability to support up to eight times more concurrent requests with a TTFT of two seconds or less sets YRCloudFile KVCache apart from native virtual Large Language Model (vLLM) performance. Furthermore, during high concurrency, the system managed to reduce TTFT by more than four times across various context lengths. This significant leap in performance indicates how effective the YRCloudFile KVCache is in enhancing resource utilization in AI processes. By simulating realistic workloads and leveraging GPU servers within a cluster, YanRong's findings reveal the tangible benefits of extending GPU memory through distributed storage solutions. The results illustrate that such enhancements not only optimize resource use but also redefine the cost structure of AI inferencing. A comparison has also been made between YRCloudFile KVCache and WEKA's Augmented Memory Grid, which similarly focuses on improving cache capacity and performance for AI applications. Overall, YanRong's advancements could have far-reaching implications for organizations seeking to leverage AI for various applications by making the processing of AI models more efficient and cost-effective. This innovation marks a crucial step towards breaking traditional compute bottlenecks and unlocking exponential improvements in AI system performance, consequently reshaping the economic landscape of AI inferencing and broadening the horizons for future developments in the field.