Comparative Analysis of Scale-Out RoCE Network Traffic Patterns and Loads in Training Large Language Models

2026-06-18 · Source: AMD ROCm Blogs · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Expert, long

Summary

A comparative analysis of scale-out RoCE network traffic patterns in large language model (LLM) training reveals critical insights for optimizing distributed AI infrastructures. The study, synthesizing data up to November 11, 2025, scrutinizes four flagship LLMs: OpenAI's GPT-4, Meta's Llama 3, DeepSeek AI's DeepSeek-V2, and xAI's Grok 4.0. While GPT-4 and DeepSeek-V2 primarily used InfiniBand, Llama 3 adopted a hybrid RoCE/IB approach, and Grok 4.0 fully embraced Ethernet-based RoCE via NVIDIA's Spectrum-X platform. Key findings show consistent bursty elephant flows from collective operations across models. Hyperscale deployments like Grok 4.0's 200,000-GPU Colossus cluster generate petabit-range network demands, orders of magnitude higher than smaller efforts. RoCE offers 30-50% cost savings over InfiniBand but necessitates advanced congestion control, adaptive routing, and telemetry to mitigate issues like low-entropy hashing and incast phenomena, with reported Model FLOP Utilization (MFU) ranging from 38-50%.

Key takeaway

For AI architects and MLOps engineers designing or optimizing LLM training infrastructure, embracing RoCE offers substantial cost savings (30-50%) compared to InfiniBand, but demands rigorous operational tuning. You must implement UDF hashing, carefully calibrate PFC headroom, and utilize topology-aware rank assignment to mitigate congestion and maximize Model FLOP Utilization. Consider in-network reduction offloads for significant collective operation efficiency gains, especially as clusters scale to tens of thousands of GPUs.

Key insights

LLM training network traffic is dominated by bursty collective operations, requiring specific RoCE optimizations for hyperscale efficiency.

Principles

• RoCE offers 30-50% cost savings over InfiniBand.
• LLM training traffic exhibits bursty elephant flows from collectives.
• Network design must align with model architecture choices.

Method

The analysis scrutinizes cluster topologies, GPU densities, parallelism paradigms, communication primitives, traffic burstiness, flow entropy, bandwidth, latency, jitter, and resilience mechanisms.

In practice

• Enable UDF hashing on Ethernet switches for ECMP path diversity.
• Calibrate PFC headroom per queue to prevent pause frame propagation.
• Use topology-aware rank assignment to reduce cross-zone traffic.

Topics

• RoCE Networks
• LLM Training
• Distributed AI
• Network Traffic Analysis
• InfiniBand
• GPU Clusters
• Congestion Control

Code references

• deepseek-ai/DeepSeek-V2

Best for: CTO, VP of Engineering/Data, AI Engineer, AI Architect, MLOps Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by AMD ROCm Blogs.