AI Systems Performance Engineering

Chapter 19. Dynamic and Adaptive Inference Engine Optimizations

Ultralarge language model (LLM) inference on modern hardware requires dynamic runtime adaptation to achieve both high throughput and low latency under varying conditions. A static “one-size-fits-all” approach to model-serving optimizations is no longer sufficient.

Instead, state-of-the-art model serving systems use adaptive strategies that adjust parallelism, numerical precision, CUDA-kernel scheduling, and memory usage on the fly. This chapter explores these advanced techniques, including dynamic parallelism switching, precision scaling, real-time cache management, and reinforcement learning (RL)-based tuning.

This chapter provides best practices for ultrascale LLM inference, teaching you how to orchestrate an engine that monitors its own performance and adapts in real time to maximize efficiency.

Adaptive Parallelism Strategies (TP Versus PP Versus Hybrid)

Massive LLMs require model parallelism, such as tensor and pipeline—or a hybrid approach—to spread computation across multiple GPUs. Each approach has benefits and drawbacks. Table 19-1 summarizes the recommended parallelism strategies for specific inference traffic patterns.

Table 19-1. Summary of common inference traffic patterns mapped to the recommended parallelism strategy

Inference traffic p	Recommended parallelism	Rationale
Many short requests (< 256 tokens, high RPS)	Data parallel/replica scaling	Minimizes inter-GPU communications; each GPU runs ...