Why is processing a 500-page book with the GPT API taking so long?

Below are some points to consider:

Expected Behavior vs. Potential Issues

• Volume of Tokens & Request Overhead:
  Processing a 90,000-word book inherently involves a very large number of tokens. If your design makes one or a few large requests rather than many small ones, the total processing time may add up due to each token’s processing latency. GPT-4 in particular is known to be more accurate but may process tokens more slowly compared to GPT-3.5.

• Sequential vs. Parallel Requests:
  If your tool sends requests sequentially, the cumulative latency can become significant. It may be worthwhile to review your request strategy – splitting tasks into more granular operations that can run concurrently might help reduce overall processing time.

Suggestions for Expediting the Process

• Optimize Token Usage:
  Following tips from latency optimization guides, minimize token usage where possible (for example, using shorter field names in JSON structures or reducing verbosity in prompts) to shave off a small amount per request. While each saved token might seem trivial, it can add up over large volumes.

• Fine-Tune Task Allocation:
  Consider whether every task requires GPT-4’s capabilities. For tasks with a well-defined structure (like standard translation instructions or editing text with predictable outcomes), a fine-tuned GPT-3.5 could suffice. This not only speeds up processing but also reduces costs.

• Batch or Parallelize Requests:
  Evaluate breaking the content into smaller chunks that can be processed in parallel, instead of one massive sequential request. This can help utilize available concurrency to reduce overall processing time.

• Review Your API Usage:
  Check if you can make use of asynchronous processing or caching of certain intermediate steps to avoid reprocessing the same sections repeatedly.

Impact of Increased GPT-4 Traffic

• Direct Effects:
  GPT-4 tends to be slower in token processing compared to GPT-3.5. If you’re already experiencing long processing times at the current load, additional traffic might worsen the latency per request because of the inherent trade-offs in using a more sophisticated model.

• Traffic Management Considerations:
  However, the effect of increased traffic isn’t solely about the model’s speed – it can also be affected by the API infrastructure. High demand might increase wait times or queue lengths, so managing the request rate or even shifting some loads to GPT-3.5 when appropriate can be beneficial.

Summary

While processing a large text can naturally require significant time, you likely have opportunities to optimize:

• Adjust the granularity of requests.
• Lower the token count per request using efficient data structures.
• Consider mixing models (e.g., using GPT-3.5 when possible).

Increasing traffic on GPT-4 could indeed exacerbate delays, so system architecture and load management should be reviewed carefully to strike the right balance between quality and latency.