The rapid growth in both computational density and scale in AI models in recent years motivates the construction of an efficient and reliable dedicated network infrastructure. This paper presents the design, implementation, and operation of Meta's Remote Direct Memory Access over Converged Ethernet (RoCE) networks for distributed AI training. Our design principles involve a deep understanding of the workloads, and we translated these insights into the design of various network components: Network Topology - To support the rapid evolution of generations of AI hardware platforms, we separated GPU-based training into its own "backend" network. Routing - Training workloads inherently impose load imbalance and burstiness, so we deployed several iterations of routing schemes to achieve near-optimal traffic distribution. Transport - We outline how we initially attempted to use DCQCN for congestion management but then pivoted away from DCQCN to instead leverage the collective library itself to manage congestion. Operations - We share our experience operating large-scale AI networks, including toolings we developed and troubleshooting examples.

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.