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Innovation Demands Compute: How to Enable ML Productivity and Efficiency
- Meta examines the implications of AI innovation on its compute resources and develops ways to measure and improve productivity and efficiency to solve the problem. Meta's largest model yet, training Llama 3.1 405B on over 15 trillion tokens was a major challenge. GenAI performance is heavily reliant on scale, necessitating significantly more resources compared to other types of AI. Investing in productivity and efficiency can positively impact return on investment. Productivity represents the relationship between outputs and resources, while efficiency represents how well the inputs are used. Analytics at Meta evaluates technological and human levers for improving productivity and efficiency.
- Meta's vision is to build AGI that is open and built responsibly. To achieve that, the company must have the required capacity to build superclusters dedicated to the state-of-the-art LLMs and advance in new product spaces. The shortage of GPUs has implications for all AI companies, including Meta. AI powers Meta's various products, such as the feed ranking algorithm, face effects filters, and Reality Labs' immersive AR. Breakthroughs in GenAI enabled Meta to create AI-powered creative assistants like Imagine.
- The scaling laws for neural language models mean that GenAI performance is heavily reliant on scale, including the number of model parameters, the dataset size, and the amount of compute. To lead in the AI space means to invest more capacity in it. Efficiency optimizes the value of the capacity investment. Improving efficiency also increases productivity, since the same levers drive both areas. Meta evaluates both technological and human levers to improve productivity and efficiency.
- Analytics plays a crucial role in identifying opportunities and measuring impact so that everyone at Meta can continue to innovate. Analytics at Meta has built strong data foundations to track metrics at various levels of abstraction, from system logs to MLE behavior. Meta is making significant strides in operating AI at its scale, and it is well on its way to achieving its goals in the pursuit of AGI.
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