Hidden Rate Limits: How Providers Throttle LLM Throughput During Peak Demand

The article discusses the challenges of scaling Language Model APIs (LLM), particularly due to resource constraints on GPU throughput. It notes that most providers have a cap on their overall throughput, leading to variable demand and hidden rate limits, such as throttling speed to expand capacity. A study of leading LLM providers like GPT4 revealed up to a 40% difference in average speed based on the time of day, which can significantly impact the quality of LLM applications during peak hours.

The article also compares different LLM providers, noting that the highest quality model, Claude-3-Opus, is also the slowest, while the Flyflow fine-tuned model outperforms the rest. Flyflow uses fine-tuning to optimize for speed and cost while maintaining quality, offering access to over 15 open source and closed source models. It uses the collected requests/responses to fine-tune a custom model that matches the base foundation model's quality while increasing speed and reducing cost.

Key takeaways:

LLM API providers often have rate limits and can throttle speed during peak demand times, leading to slower performance.
A recent investigation showed up to a 40% difference in average speed from leading LLM providers, with performance varying greatly depending on the time of day.
When comparing providers, there are trade-offs to consider between speed, cost, and quality of the model. The highest quality model in the study was also the slowest.
Flyflow uses fine-tuning to optimize for speed and cost while maintaining quality, offering access to over 15 open source and closed source models.

Hidden Rate Limits: How Providers Throttle LLM Throughput During Peak Demand - Flyflow

Key takeaways:

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