3 · High-Throughput Inference

3. GPU Inference Optimization & Dynamic Batching

Triton inference server, TensorRT quantization, and memory-bound vs compute-bound throughput.

11 min read · 2 MCQs

Dynamic Batching

Individual inference requests arrive sporadically. Dynamic batching queues incoming requests on the GPU within a microsecond window (e.g. 5ms), packing them into a single tensor matrix multiplication to maximize GPU compute saturation without violating SLA latency.

Model Quantization (FP32 -> FP16 -> INT8)

Using TensorRT or ONNX Runtime to quantize model weights from 32-bit floating point to 8-bit integers cuts VRAM memory footprints by 75% and doubles throughput with negligible loss in validation accuracy.

Chapter quiz

2 questions · pass mark 75%

1. How does dynamic batching increase GPU utilization?
2. What is the primary benefit of INT8 model quantization?