The DualPath Breakthrough: Solving Storage Bandwidth in Agentic Inference

Introduction: A New Challenge in the Era of Agentic Inference — Storage Bandwidth

Recently, AI technology has moved beyond simply answering questions to entering the "Agentic" phase—where models plan autonomously and use tools to solve complex problems. In this agentic LLM (Large Language Model) environment, model sizes are growing massive, and the data throughput generated during complex reasoning processes is increasing exponentially. However, we face a harsh reality: compared to the immense computational power of these models, the bandwidth available for moving data is severely lacking.

In existing computer architectures, the imbalance between the data flow required to load/save model parameters and the physical storage capacity acts as a chronic bottleneck. In other words, no matter how powerful a GPU may be, if the speed of fetching data from storage cannot keep up, overall inference performance will inevitably stagnate. This "storage bandwidth" problem serves as one of the greatest hurdles in the next-generation AI environment, where agents must think and act in real-time.

DeepSeek has focused its attention precisely on this issue. Their latest research, "DualPath: Breaking the Storage Bandwidth Bottleneck in Agentic LLM Inference," was designed to break through this chronic bottleneck and maximize agentic inference performance. The core objective of this technology is to fundamentally redesign data flow efficiency, turning the potential of next-generation AI models into reality.

Body 1: The Mechanism and Innovation of the DualPath Architecture

As the name suggests, DeepSeek's proposed DualPath is an innovative architecture focused on solving the "Storage Bandsowdth Bottleneck" by optimizing data paths. In traditional methods, the latency incurred when exporting model weights or intermediate computation results (such as KV cache) to storage was a major variable determining total inference time. DualPath was designed specifically to strike at these bottlenecks within the data flow.

The core of the DualPath architecture lies in maximizing efficiency through optimized data flow. Rather than simply providing a physical solution like using faster storage devices, it fundamentally changes the structural design so that the model can exchange necessary data through the most efficient paths. Specifically, by optimizing KV cache (Key-Value Cache) management—which plays a crucial role in agentic inference—and the movement of data between memory and computation units, it effectively distributes the storage read/write load generated during large-scale model operations. This makes it possible to reduce unnecessary waiting time and ensure a constant supply of data so that compute units can work without idle periods.

Consequently, DualPath demonstrates a unique design that diverges from traditional linear, single-channel data transfer methods. By diversifying the paths through which data moves and intelligently controlling the data flow at each stage, it resolves the imbalance between storage and computing devices. This goes beyond merely increasing speed; it implements an optimal architectural design that allocates hardware resources with maximum efficiency.

Body s2: Impact on Agentic Workflows

The innovation of DualPath technology has a decisive impact on "agentic workflows"—the actual way AI operates. An agent task does not end with a single inference; it involves multiple stages of thought to arrive at a result. The complex loop structures generated during this process cause data volume to explode. However, if an optimized data flow is in place, the speed at which an agent solves problems can increase dramatically.

In particular, as storage bandwidth issues are resolved, the improvement in real-time inference performance becomes highly tangible. This is because the latency occurring while a model thinks and acts during user interaction is minimized. By ensuring "real-time" capability—allowing agents to interact with humans and provide instantaneous feedback—it revolutionizes the perceived responsiveness of AI.

In the ecosystem of cutting-edge models like DeepSeek-V4, the value of DualPath technology shines even brighter. DeepSeek leads various research areas, including evolving multimodal understanding and generation models (such as Janus-Pro, DeepSeek-VL2, etc.). For these complex models to operate seamlessly, a powerful data infrastructure is essential, and DualPath serves as the technical foundation that unlocks the full potential of these large-scale models.

Conclusion: A Revolution in Data Flow Toward AGI

The DualPath technology goes beyond simply increasing storage speed; it provides a key to developing AGI (Artificial General Intelligence), the next-generation AI engine. To become a truly intelligent system, the key lies in how one manages the massive data flow generated during the continuous process of processing information and thinking, much like a human. DualPath provides a powerful tool to control this massive wave of data.

Optimizing storage bandwidth offers crucial technical implications for building future agentic systems. It demonstrates that bridging the gap between hardware and algorithms and securing efficient data flow is directly linked to the realization of intelligence. This is an essential virtue for future AI systems, not only for enhanced performance but also in terms of resource utilization.

Since its founding in 2023, DeepSeek's journey toward making AGI a reality has been highly encouraging. By breaking through the limits of data flow via innovative technologies like DualPath, DeepSeek is playing a pivotal role in moving one step closer to the universal artificial intelligence humanity has dreamed of. We are now witnessing the most dynamic era where intelligence is manifested into reality.