AMD: Analysis of RoCE Network Traffic Patterns in Large Language Model Training

AMD has published a comparative analysis of the network traffic patterns generated during the training of four large language models in scale-out GPU clusters. The study covers GPT-4, Llama 3, DeepSeek-V2, and Grok 4.0, and provides concrete guidance for engineers designing modern AI infrastructure.

What Is RoCE and Why Is It Critical for Distributed Training?

RoCE (RDMA over Converged Ethernet) is a networking technology that enables direct memory-to-memory communication between GPU nodes — without involving the CPU. The result is dramatically lower latency and higher throughput compared to classical TCP/IP stacks. This characteristic makes RoCE the standard for high-performance AI clusters where hundreds or thousands of GPUs must continuously exchange gradients and activations.

Different Models, Different Traffic Patterns

The analysis reveals that GPT-4, Llama 3, DeepSeek-V2, and Grok 4.0 generate significantly different network profiles during training. Architectural differences — such as the number of attention heads, batch size, and parallelization strategy — directly affect how much traffic, in what bursts, and with what latency distribution the network must handle. A uniform cluster design that “works for all” does not exist; each model imposes different demands on switch topology, buffer sizes, and QoS policies.

AMD Instinct’s Strategic Position in AI Infrastructure

By publishing this study, AMD positions its Instinct accelerators as a technically grounded alternative to NVIDIA infrastructure. Concrete traffic pattern data enables engineers to optimize the network layer for the ROCm ecosystem with the same precision as for CUDA-based clusters. The study targets cloud providers, research institutions, and companies building private AI training clusters that seek greater hardware independence.