Hardware

AMD Analyzes RoCE Network Traffic in LLM Training

AMD's analysis reveals that Grok 4.0's 200,000-GPU cluster generates network loads orders of magnitude higher than smaller-scale efforts.

Image: AMD

AMD has published a comparative analysis of scale-out RoCE network traffic patterns and loads during the training of large language models. The study highlights the critical role of high-performance networks in managing the massive data exchanges required for distributed AI workloads. The report examines four flagship LLMs, including OpenAI’s ChatGPT 4.0, Meta’s Llama 3, DeepSeek AI’s DeepSeek-V2, and xAI’s Grok 4.0, to understand how different architectures affect network behavior and performance. The analysis is based on a comprehensive review of technical reports, peer-reviewed papers, and industry analyses up to November 11, 2025. It explores various dimensions, such as cluster topologies, GPU densities, and communication primitives, to provide insights into optimizing network efficiency for large-scale AI training.

The report underscores that while GPT-4 and DeepSeek-V2 primarily used InfiniBand for its low-latency guarantees, Llama 3 adopted a hybrid RoCE and IB approach, and Grok 4.0 fully embraced Ethernet-based RoCE through NVIDIA’s Spectrum-X platform. Despite these differences, the core traffic patterns—characterized by bursty elephant flows from collective operations like AllReduce and All-to-All—remain consistent across models, driven by shared distributed training frameworks such as PyTorch, JAX, and NCCL. The study also notes that hyperscale deployments, such as Grok 4.0’s 200,000-GPU Colossus cluster, result in network loads that are significantly higher than smaller-scale efforts, with per-server bandwidth peaking at terabits per second and aggregate fabric demands reaching the petabit range.

The analysis provides a historical context of LLM development, tracing the evolution from early transformer-based models like BERT in 2018 to contemporary models exceeding a trillion parameters. It highlights how the exponential growth in model parameters and computational requirements has necessitated scale-out paradigms that distribute workloads across vast GPU arrays. The report also delves into the technical underpinnings of RoCE, emphasizing its role in AI infrastructures through zero-copy data transfers over Ethernet fabrics.

Source: amd

Key points

AMD's analysis reveals that Grok 4.0's 200,000-GPU cluster generates network loads orders of magnitude higher than smaller-scale efforts.
The report highlights that GPT-4 and DeepSeek-V2 predominantly leveraged InfiniBand for its low-latency guarantees.
Llama 3 adopted a hybrid RoCE and IB approach, while Grok 4.0 fully embraced Ethernet-based RoCE through NVIDIA’s Spectrum-X platform.
Core traffic patterns, characterized by bursty elephant flows from collective operations like AllReduce and All-to-All, remain consistent across models.
Hyperscale deployments, such as Grok 4.0’s 200,000-GPU Colossus cluster, result in network loads that are significantly higher than smaller-scale efforts.
RoCE’s integration in Meta and xAI’s infrastructures highlights its economic advantages, potentially reducing costs by 30-50% over IB.

Source: AMD Read the original →

WRITTEN BY

Sam Bergstrom

AI Infrastructure & Hardware

Sam specializes in AI chips, data centers, and training infrastructure.