Part 11/13:
Similarly, the webinar demonstrated benchmarking results, highlighting how optimized models in FP16 outperform FP32 versions, with throughput increasing by multiple folds under higher loads.
Performance Benchmarks & Metrics
Latency Reduction: Switching from Hugging Face's default deployment to Triton + TensorRT shows latency improvements of 5x.
Throughput Scalability: Increasing concurrent requests from 1 to 4 boosts GPU utilization from ~50% to ~92%, substantially increasing requests per second—from 61 images/sec (FP32) to over 1200 images/sec (FP16).
Dynamic Adjustment: Model size shrinks in FP16, storage requirements reduce, and throughput improves, demonstrating the efficiency of model optimization techniques.