Scaling AI Inference for High Traffic

As your AI product grows, you will inevitably hit scaling walls. Traditional API scaling isn't enough when your bottleneck is GPU computation time.

Batching and Queueing

Don't execute every request synchronously. Use a task queue (like Celery or Redis Streams) to manage inference requests. By batching smaller requests into single GPU passes, you can increase your throughput by 5-10x.

Model Distillation

If you are using massive models like GPT-4, consider distilling that knowledge into a smaller, cheaper model (like Llama 3 8B or Phi-3) for your most common tasks. You can achieve 90% of the accuracy for 10% of the cost and 1/10th the latency.