DeepSeek released new paper: DualPath: Breaking the Storage Bandwidth Bottleneck in Agentic LLM Inference

A research consortium from Peking University, Tsinghua University, and DeepSeek-AI has published a breakthrough paper introducing DualPath, a new inference architecture designed to overcome a critical performance bottleneck in agentic large language models. The system specifically targets the KV-Cache (Key-Value Cache) storage I/O bandwidth limitation that emerges when LLMs operate in agentic modes—where models perform sequential reasoning, tool calls, and multi-step planning. Traditional inference systems struggle with the irregular memory access patterns and massive KV-Cache sizes generated by these agentic workflows, leading to underutilized compute and throttled throughput. DualPath rearchitects the data flow and memory hierarchy to decouple computation from storage constraints, allowing for more parallel processing and significantly reduced latency.

The technical innovation lies in DualPath's dual data path design, which separates high-frequency, small-sized metadata accesses from bulk KV-Cache transfers. This separation reduces memory bandwidth pressure by 40-60% and enables up to 2.5x higher throughput compared to existing systems like vLLM or TensorRT-LLM under agentic workloads. For developers and enterprises, this means AI agents that can handle longer reasoning chains, more complex tool orchestration, and real-time interactive tasks become far more economically viable to deploy at scale. The research signals a shift from optimizing purely for static prompt-completion to designing systems for dynamic, stateful AI applications, paving the way for the next generation of autonomous AI assistants and copilots.