Handling High-Throughput Inference

Summary

Techniques (batching, model parallelism, dedicated inference servers) for serving very high volumes of model requests efficiently without linearly scaling cost.

How to Learn This

1Learn dynamic batching and how it improves GPU utilization for inference servers.
2Read about dedicated inference-serving tools (e.g. vLLM, TensorRT-LLM) at an overview level.
3Understand the trade-off between batching for throughput and the added latency it introduces.

Designing a Content Moderation System

Choosing Between Fine-Tuning and RAG

More topics in System Design for AI Applications

Designing Scalable AI Systems Latency & Cost Optimization Caching LLM Responses Load Balancing AI Workloads Designing a RAG System at Scale Designing a Recommendation System Designing a Content Moderation System Choosing Between Fine-Tuning and RAG

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