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Behind the scenes scaling ChatGPT - Evan Morikawa at LeadDev West Coast 2023
Discover the behind-the-scenes scaling strategies behind ChatGPT, from optimizing performance with replicated GPU architecture to developing effective abuse detection and mitigation strategies, in this enlightening talk by Evan Morikawa.
- GPUs are the bottleneck for scaling language models like ChatGPT.
- There are three major challenges when dealing with abuse on the system: keeping the essence of a startup team, adapting to constraints, and ensuring proper safety mitigations.
- ChatGPT uses replicated GPU architecture and caching to optimize performance.
- The team had to delay certain launches and product features due to GPU capacity constraints.
- Windows Azure public regions were used for scaling.
- Overtime, more people were attempting to exploit the API, requiring better abuse detection and mitigation strategies.
- In some cases, it was difficult to identify abuse due to traffic patterns not matching API signatures.
- Small, nimble teams were able to stay ahead of larger teams in terms of adaptation and innovation.
- Time and speed are critical when developing language models, requiring a focus on efficient computation and memory.
- Larger language models also lead to more math operations, making memory more critical.
- OpenAI’s mission is to prevent AI from powering mass disinformation campaigns, and the company is committed to maintaining a “low-key research preview” for some products.
- The company has discovered more people attempting to exploit the API and is working to improve abuse detection and mitigation.
- Newer, riskier products will go through multiple stages of rollout as the company identifies and mitigates risks.
- The future of language models and AI is uncertain, but requiring attention to details like memory, computation, and batch size.
- Teams will need to adapt to changing constraints and stay nimble to remain competitive.
- Better cache management and optimized compute are crucial for scaling language models.
- OpenAI is working with chip manufacturers and data centers to improve GPU performance and availability.
- Language models will continue to evolve, requiring ongoing attention to abuse detection and mitigation.
- Managing teams and maintaining a “startup culture” are crucial for OpenAI’s success.