Infrastructure for the Agent Era: New Storage Demands from Long-History Conversations

Introduction: Agent Workloads Are Reshaping Storage Demands

AI applications are evolving from single-turn Q&A to multi-turn, multi-modal, long-history conversations. Agent-type applications—such as autonomous programming assistants, video generation workflows, and enterprise-grade private-deployment intelligent customer service—require continuous maintenance of context windows, with session lengths expanding from thousands of tokens to millions or even billions. This shift directly impacts traditional storage architectures: KV Cache loading during model inference, training checkpoint saving, and shared state management during multi-agent collaboration impose unprecedented demands on storage latency, bandwidth, and concurrency.

Storage is no longer a "data warehouse" but a critical bottleneck for Agent inference latency and throughput. This article analyzes the new challenges facing infrastructure in the Agent era from a storage perspective and explores feasible technical paths based on measured data.

Storage Bottlenecks in Long Sessions: From KV Cache to Multi-Modal Data

Each inference of an Agent requires loading the historical session's KV Cache from storage. Taking a 480B-parameter MoE model as an example, a single long-context cold start requires reading hundreds of GB of KV Cache data. Traditional NFS-based network storage solutions, when faced with such large-capacity, high-concurrency random reads, can result in TTFT (time to first token) reaching tens or even hundreds of seconds.

In joint tests on the Mingxin FX100 and AMD MI308X platform (measured, report R2), the 480B model under TP8 configuration saw TTFT p50 reduced from a baseline of 10.17–35.73 seconds to 7.53–26.35 seconds, a reduction of 26–32%. This improvement directly stems from the low-latency NVMe-oF architecture on the storage side: the FX100, with single-port 100Gb bandwidth and 16M IOPS random read capability, compresses KV Cache loading time to the millisecond level.

For video generation Agents (e.g., workflows based on ComfyUI + LTX-Video), storage demands are more complex. Multi-level reads and writes of model weights, intermediate frame caches, and final output files require storage to simultaneously offer high sequential write bandwidth and low random read latency. In private deployment scenarios where user data does not leave the domain, the storage system must also support fine-grained permissions and encryption.

Measured Boundaries of Storage Performance: From Load Acceleration to Throughput Improvement

Agent applications' demands on storage are not limited to latency but also include throughput. During multi-agent concurrent inference, storage must serve multiple KV Cache read requests simultaneously, directly testing IOPS and bandwidth.

In measured tests (report R3), with the 480B model under a full-machine TP4×2 configuration, the FX100 achieved a 35–36% throughput improvement (from a baseline of 4.1 tok/s to 74.9 tok/s, a speedup of 18.3×). This data reveals a key fact: when storage is no longer a bottleneck, GPU utilization can be significantly increased, bringing overall model inference throughput close to theoretical limits.

For training scenarios, iterative updates of Agent models require frequent checkpoint saving. Measured tests (report R1) show that in 8-card 32B LoRA training, the time to save a 65.6GB full model snapshot dropped from 178 seconds to 94 seconds, with sustained write bandwidth increasing from 3.26 GB/s to 6.40 GB/s (+96%). This means that in the rapid iterative development of Agent models, every 1-second reduction in storage latency can save hours of waiting time.

Storage Architecture Choices for Private Deployment

Enterprise-grade Agent applications typically require private deployment to ensure data sovereignty and compliance. This demands that the storage system support high performance, high reliability, and flexible scalability, while being compatible with existing GPU clusters (e.g., Huawei Atlas 910B, AMD MI300X, etc.).

Tests on the Huawei 910B platform (measured, report R9) show that the FX100, compared to the NFS baseline, achieves a model loading acceleration of 6.2–9.3× (DeepSeek-32B from 691s to 112s, DeepSeek-70B from 1399s to 150s). This gap is particularly significant when Agents frequently switch models or restart services: each loading saves minutes, meaning a substantial increase in Agent service availability.

Storage architecture choices must also consider the burstiness of Agent workloads. During long-session inference, storage load can spike from low to full capacity within seconds. The FX100's NVMe-oF architecture, using RDMA and RoCEv2 protocols, maintains stable latency and bandwidth under high concurrency, avoiding inference timeouts caused by storage jitter.

Conclusion

In the infrastructure of the Agent era, storage is no longer a supporting player. From KV Cache loading to model checkpoint saving, from video generation to multi-agent collaboration, storage latency and bandwidth directly determine the response speed and throughput capability of Agent applications. Mingxin Technology continues to iterate on the FX product line. The FX100 has demonstrated its performance advantages in long-session, high-concurrency scenarios through multi-platform measured tests. For teams building Agent infrastructure, we welcome you to verify storage optimization effects in your actual workloads through an approximately 10-week gated joint test (G1: arrival acceptance / G2: single-machine baseline / G3: main gate: TTFT reduction ≥25%, throughput +29–40% measured in-band / G4: 72-hour stability).