Scaling LLM Inference to 10M Requests Per Day
When we set out to build an LLM inference infrastructure capable of handling 10 million requests per day, we knew we could not simply throw more GPUs at the problem. The economics would not work, and the latency requirements of real-time applications demand intelligent architecture.
Continuous Batching
Traditional inference servers process requests one at a time, leading to poor GPU utilization. Our inference server implements continuous batching, where new requests are added to the current batch as soon as earlier requests complete. This increases throughput by up to 8x compared to static batching.
Quantization Pipeline
We developed an automated quantization pipeline that reduces model precision from FP16 to INT4 with minimal accuracy loss. This pipeline uses calibration datasets specific to each deployment and applies different quantization strategies to different layers based on their sensitivity.
Distributed Serving
For large models that do not fit on a single GPU, we built a distributed serving layer that shards models across multiple devices. Our custom communication protocol minimizes inter-device latency and supports heterogeneous hardware configurations.