How to Supercharge AI Inference: End‑to‑End Acceleration Strategies and Baidu’s AIAK‑Inference

Overview

This article is the third session of Baidu Baige’s "Cloud‑Native AI" technical open class, focusing on an end‑to‑end analysis of the AI inference process, its pain points, typical industry acceleration approaches, and Baidu Intelligent Cloud’s practical solutions.

AI Inference Pain Points

AI inference involves deploying a trained model on compute hardware and serving it via HTTP/RPC. Two main participants are AI algorithm engineers, who need fast model deployment and service, and infrastructure engineers, who manage heterogeneous GPU clusters and aim for high resource utilization. Their pain points include slow model serving for engineers and under‑utilized expensive GPU resources for infrastructure teams, with no dedicated tools to keep GPUs busy or maximize SM (Streaming Multiprocessor) usage.

Industry Acceleration Solutions

Effective acceleration starts by defining optimization targets and understanding GPU architecture: multiple SMs each containing ALUs and Tensor Cores. GPU tasks are triggered by the CPU, and optimal performance requires keeping the GPU continuously busy and maximizing SM utilization. Metrics such as GPU utilization and the finer‑grained SM utilization are used, with SM utilization being a more precise indicator of efficiency.

Three Optimization Categories

Model Simplification : Quantization (offline and quant‑aware training), pruning, knowledge distillation, and Neural Architecture Search reduce model compute before execution.

Operator Fusion : Merges many small operators into larger ones to reduce kernel launch overhead and memory traffic; examples include NVIDIA’s FasterTransformer for Transformer models.

Single‑Operator Optimization : Tailors GPU kernel implementations for specific operators, such as GEMM, using libraries like NVIDIA Cutlass.

AIAK‑Inference Acceleration Suite

AIAK‑Inference is Baidu Intelligent Cloud’s AI inference acceleration suite, part of the Baidu Baige solution. It optimizes GPU inference latency and throughput for any GPU resource purchased on Baidu Cloud.

The architecture consists of four layers:

Graph Ingestion: Converts dynamic/static graphs to inference‑friendly static graphs.

Backend Abstraction: Integrates multiple acceleration backends and selects the best via timing.

Specific Acceleration Backends: Supports open‑source backends (e.g., FastDeploy) and Baidu’s proprietary backend that combines graph optimization, conversion, and runtime acceleration.

Operator Library: Provides industry‑leading operators and custom, scenario‑specific operators via the AIAK‑OP library.

Key features include seamless multi‑backend integration with performance‑based selection and deep scenario‑specific optimizations.

Acceleration principles mirror industry approaches:

Graph simplification: quantization, pruning, distillation, NAS, mathematical equivalence substitution, dead‑code removal.

Operator fusion: memory‑intensive fusion, GEMM/Conv tail fusion, back‑to‑back GEMM fusion.

Single‑operator optimization: scheduling, memory access tuning, templated kernels (e.g., Conv operator memory‑instruction reduction leading to 3% end‑to‑end gain).

Using AIAK‑Inference

Installation is available via an acceleration Docker image or a wheel package. After environment setup, a one‑line optimization script using aiak_inference.compile (or aiak_inference.optimize) converts a TorchScript/SavedModel model into an optimized version without changing deployment code.

Demo with ResNet‑50 (FP32, batch size 1) on a T4 GPU shows baseline latency of 6.73 ms, reduced to 3.54 ms after optimization. Six typical CV models achieve 40‑90% latency reduction.

Conclusion

The session demonstrates how end‑to‑end AI inference acceleration—through model simplification, operator fusion, and single‑operator tuning—combined with Baidu’s AIAK‑Inference suite can dramatically improve inference performance and GPU utilization.