Meet Cisco Intelligent Packet Flow

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Updated:September 9, 2025

Bias-Free Language

The documentation set for this product strives to use bias-free language. For the purposes of this documentation set, bias-free is defined as language that does not imply discrimination based on age, disability, gender, racial identity, ethnic identity, sexual orientation, socioeconomic status, and intersectionality. Exceptions may be present in the documentation due to language that is hardcoded in the user interfaces of the product software, language used based on RFP documentation, or language that is used by a referenced third-party product. Learn more about how Cisco is using Inclusive Language.

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Updated:September 9, 2025

Table of Contents

 

 

AI and high-performance computing workloads impose unprecedented demands on network infrastructure. They require exceptional bandwidth, ultra-low latency, and intelligent traffic management to maintain GPU efficiency and ensure seamless performance. Traditional methods like static equal-cost multi-path (ECMP) routing cannot keep up with the demanding, bursty, and synchronized east-west traffic patterns typical of AI environments leading to congestion, job delays and resource inefficiencies.

Overview

A diagram of a computer networkAI-generated content may be incorrect.

Figure 1.  Powering AI fabrics with Intelligent Packet Flow with Cisco Nexus

Feature and Benefits

Table 1.        Cisco Intelligent Packet Flow offers a range of benefits to meet the unique demands of AI/ML networks, including: 

Feature

Benefit

Dynamic Load Balancing

  Maximizes GPU efficiency by evenly distributing data across available paths.

Adaptive Traffic Steering

  Reduces tail latency by avoiding congestion during peak load. 

Real-time fault detection
& rerouting

  Maintains uninterrupted AI/ML job performance despite network disruptions. 

Embedded telemetry

  Enhanced Observability that deep and actionable visibility into live network behavior. 

Policy-based load balancing

  Enables tailored and simultaneous traffic management for training, inference and storage workloads.

Trends and Challenges

The rising tide of AI workloads

The rapid adoption of AI and machine learning is reshaping data center architecture. AI training requires extensive GPU clusters and frequently generates synchronized east-west traffic patterns, placing immense pressure on networks. Traditional routing methods fail to keep up, causing latency spikes and inefficient resource utilization. 

When AI workloads scale across rows of racks or multi-site clusters, one poorly managed burst of traffic can disrupt the entire fabric, leading to cascading performance issues. This demands a networking solution that delivers both precision traffic engineering and robust congestion control. 

Cisco Intelligent Packet Flow addresses these trends, enabling networks to meet the performance demands of AI while maintaining fair resource allocation, optimal throughput and predictable performance at scale. 

Key features

Table 2.        Cisco Intelligent Packet Flow combines cutting-edge features to deliver precision traffic management in large-scale AI/ML environments. 

Key components

Platforms supported

Dynamic load balancing (DLB)

Intelligent flowlet-based traffic distribution ensures efficient utilization of all available paths. 

Weighted cost multi-path (WCMP)

Path weighting based on real-time telemetry dynamically allocates workloads to higher-capacity links, ensuring optimal throughput. 

Per-packet load balancing

Packet spray maximizes utilization for high-throughput GPU-to-GPU communications. 

Policy-based load balancing

Assigns specific traffic-handling strategies to mixed workloads based on ACLs, DSCP markings, or RoCEv2 headers, creating custom-fit efficiency for diverse needs. 

Use case

Table 3.        Cisco Intelligent Packet Flow enables:

Use case

Application

Real-time insights

Cisco Intelligent Packet Flow offers rich telemetry features, including microburst detection, congestion signaling, and in-band network telemetry (INT), providing deep visibility into network behavior at any scale. 

Scale-out training

Adaptive load balancing ensures efficient GPU communication across racks, minimizing congestion and reducing job completion time for large-scale LLM training.

Inference and storage

Policy-based load balancing optimizes mixed workloads, enabling seamless simultaneous training, inference, and data ingestion without compromising network performance.

Autonomous recovery

By detecting issues like link failures in real time and rerouting around degraded paths, Intelligent Packet Flow minimizes disruptions, ensuring continuous performance.

 

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