Meta has released Llama 3, its most capable and openly-available large language model to date. The model is set to enable new workflows, such as synthetic data generation and model distillation, with unmatched flexibility and state-of-the-art capabilities that rival the best closed-source models. Meta engineers detailed every step of the process behind building and deploying Llama 3, from data acquisition and training to inference.
Joe Spisak, Product Director and Head of Generative AI Open Source at Meta, provided an overview of the history of Llama and Meta’s vision for open-source AI. Delia David, a software engineer at Meta, explained the diversity, volume, and freshness of data required for Generative AI and the processes involved in preparing different data types. Kaushik Veeraraghavan, another software engineer at Meta, discussed the immense scale at which Meta trains Llama and highlighted the investments in data centers, networking, and software that have been critical in developing Llama 3.
Meta’s Llama 3 deployment journey
Ye (Charlotte) Qia, a production engineer at Meta, shed light on the intricacies of handling inference for Llama. She emphasized the importance of optimizing and scaling LLM inference to enable large-scale product applications.
Qia introduced key parallelism techniques that help scale model sizes and context windows, influencing the inference system designs. She also addressed the practical challenges of deploying these complex serving paradigms throughout Meta’s internal cloud and heterogeneous hardware data centers. The development and deployment of Llama 3 epitomize Meta’s commitment to advancing AI technology with open-source ethos, ensuring transparency and collaboration in the AI community.
The model’s open-source nature is expected to foster a new ecosystem of developers and encourage innovative AI applications across various industries.
