DeepSeek's Dualpath Paper explained with animations (mesuvash.github.io)

DeepSeek has announced the implementation of its DualPath architecture, which significantly enhances key-value (KV) cache loading throughput during prefill-decode disaggregated serving by utilizing idle decode-side network interface cards (NICs). This innovation addresses a crucial bottleneck in modern large language model (LLM) serving, where the storage NICs for prefill engines often sit idle while decode engines run their processes. By leveraging the compute network's high bandwidth for transferring KV data, DualPath effectively converts a single-sided bottleneck into a more efficient, distributed resource, thereby optimizing the latency for agentic workloads that rely heavily on cached data for multi-turn interactions. The significance of this development lies in its ability to nearly double workflow efficiency, with DualPath achieving speedups of up to 1.87x in offline inference and 1.96x more agent runs per second in online scenarios. The architecture incorporates advanced features such as layerwise prefill and an adaptive request scheduler that balances workloads across GPU and storage operations. By ensuring that storage traffic does not interfere with critical model execution tasks, DualPath not only improves throughput but also maintains low latency metrics, even as workloads scale. This positions DeepSeek’s DualPath as a transformative approach in the AI/ML community, highlighting the potential for optimized resource utilization in high-demand environments.