DualPath: Breaking the Storage Bandwidth Bottleneck in Agentic LLM Inference (Feb 2026)

Описание: Title: DualPath: Breaking the Storage Bandwidth Bottleneck in Agentic LLM Inference (Feb 2026)
Link: http://arxiv.org/abs/2602.21548v2
Date: February 2026

Summary:
DualPath is an LLM inference system designed to solve the storage I/O bottleneck in agentic workloads, which are characterized by long-running sessions and high KV-Cache hit rates. It introduces a dual-path loading mechanism that utilizes the idle storage bandwidth of decoding engines to assist prefill engines via RDMA, coordinated by a global scheduler and a traffic manager that ensures isolation from latency-critical model communications.

Key Topics:
Agentic LLM Inference
KV-Cache Storage I/O
Prefill-Decode Disaggregation
RDMA Data Transfer
Network Traffic Isolation
Load Balancing

Chapters:
00:00 - Introduction To DualPath
01:15 - Analyzing Agentic Storage Bottlenecks
03:00 - Bridging The Hardware Gap
04:42 - Implementing Dual-Path Loading
06:43 - Pipelining Fine-Grained Transfers
08:02 - Isolating Critical Network Traffic
09:25 - Scheduling Adaptive Requests
10:06 - Measuring Online Serving Throughput
11:30 - Scaling Beyond DRAM Caching
12:57 - Optimizing The Full Stack
14:13 - Rethinking Future Node Architecture
15:35 - Shifting Toward Dataflow Inference

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