My friend, I know how frustrating hitting those ChatGPT rate limits can be when you’re in the flow generating content. As an AI architect, I’ve learned firsthand the monumental challenge of scaling advanced systems to meet this kind of exponential demand growth practically overnight.
Let me walk you through what’s going on behind the scenes and some best practices we can try for keeping your ideas flowing freely.
To start, the stats here are just jaw-dropping…
Daily Users:
Queries Per Second:
You really get a visual sense here of the hockey stick growth, practically vertical! To handle advanced NLP queries at this kind of scale requires massively powerful models (hundreds of billions of parameters trained) and thousands of specialized AI servers (GPUs/TPUs) behind the scenes – more than almost any other service online.
So when traffic spikes 100X+ over the holidays, there are inherently going to be some growing pains keeping up. The key constraints around optimizing response accuracy, latency and cost boil down to 3 limits:
Compute Power: More servers packed with expensive hardware like GPUs are needed to parallelize query processing – billions of matrix calculations per token generated!
Model Scale: Bigger, more complex models drive higher quality responses but have exponentially higher server requirements.
Memory: Context needs to be retained session by session without spilling over into storage which would crater latency.
You can start to see why traditional web scaling tactics like adding more basic servers don’t work well – the complexity is at the model and algorithmic level.
So while Anthropic executes their expansion roadmap to boost capacity, we have to accept responsibly rate limiting ourselves a bit. Here are a few tips I’ve found that help…
Take Breaks: Limit sessions to no more than 30-60 minutes. Let those models rest!
Queue Requests: For campaigns, sequence content in buckets over days rather than overflowing all at once.
Monitor Resources: Keep an eye on @ChatGPTStatus for demand surges and consider mix shift scheduling.
Supplement Carefully: We can mix in our own knowledge, research and structuring occasionally rather than purely prompts.
Harnessing this revolutionary AI safely and effectively will be a collaborative effort. But the long term possibilities make it so worth working through these temporary growing pains! Let me know if any other questions come up. Now go create 🙂