DualPath: How a New LLM Inference Architecture Breaks the Storage Bandwidth Bottleneck
1 months ago 高效码农DualPath: Breaking the Storage Bandwidth Bottleneck in Agentic LLM Inference A New Architecture That Boosts Multi-Turn AI System Performance Through Dual-Path KV-Cache Loading Introduction: When AI Agents Become Mainstream, Inference Architectures Face New Challenges Large Language Models (LLMs) are evolving from simple single-turn chatbots into intelligent agent systems capable of autonomous planning, tool invocation, and solving real-world tasks through multi-turn interactions. Whether it’s coding assistants or automated task agents, these applications all rely on multi-turn LLM inference—a long session process where context accumulates over time. This transformation brings a fundamental technical challenge: Agentic workloads become extremely I/O-intensive. Imagine an AI …
LangGraph Technical Architecture: Building Intelligent Agent Collaboration Through Graph Computing
11 months ago 高效码农LangGraph Technical Architecture Deep Dive and Implementation Guide Principle Explanation: Intelligent Agent Collaboration Through Graph Computing 1.1 Dynamic Graph Structure LangGraph’s computational model leverages directed graph theory with dynamic topology for agent coordination. The core architecture comprises three computational units: • Execution Nodes: Python function modules handling specific tasks (<200ms average response time) • Routing Edges: Multi-conditional branching system supporting O(n²) complexity expressions • State Containers: JSON Schema-structured storage with 16MB capacity limit （Visualization: Multi-agent communication framework, Source: Unsplash） Typical workflow implementation for customer service systems: class DialogState(TypedDict): user_intent: str context_memory: list service_step: int def intent_analysis(state: DialogState): # Intent recognition …