[Introduction] DistilBERT Inference Optimization Practice: A Guide to Performance Leap from FP32 to INT8 Quantization

The LLM_Inference_Optimisation project focuses on the pain points of inference optimization, taking DistilBERT as the research object to systematically explore the optimization path from FP32 to INT8 quantization. It covers quantization techniques, ONNX conversion, and edge deployment tuning, providing detailed benchmark data and reusable methodologies to help engineers balance accuracy and efficiency.

Background: Urgency of Inference Optimization and Choice of DistilBERT

Practical Urgency of Inference Optimization

When large models move from the lab to production, there is a gap between training performance and inference experience, making inference optimization a hot topic in AI engineering.

Why Choose DistilBERT?

As a distilled version of BERT, DistilBERT retains over 95% of the performance, reduces parameter count by 40%, and increases inference speed by 60%. With a moderate scale (66M parameters), it is suitable for edge deployment and learning research.

Methodology: Precision Format Spectrum and ONNX Runtime Optimization

Comparison of Precision Formats

• FP32: Baseline format with highest accuracy but high memory and computational overhead;
• FP16: Halves storage and computation requirements, supported by GPU hardware acceleration, but numerical stability needs attention;
• INT8: Compresses volume and bandwidth to 1/4, with significant hardware acceleration; strategies like dynamic range quantization, static calibration, and QAT are needed to reduce accuracy loss.

ONNX Runtime Optimization

Through graph optimization (operator fusion), memory layout optimization, operator selection, etc., CPU inference latency is reduced by 30-50% compared to the original PyTorch.

Methodology: Special Considerations for Edge Deployment

Edge devices have characteristics of limited resources, heterogeneous computing, and high real-time requirements:

• Limited resources: Adapt via pruning, quantization, dynamic batching;
• Heterogeneous computing: Map different parts of the model to optimal units like CPU/GPU/NPU/DSP;
• Real-time performance: Reduce memory copies, optimize preprocessing, and use streaming inference to lower latency.

Evidence: Rigorous Benchmark Methodology

Test Dataset

Diverse text samples (different lengths, domains, complexities) ensure generalization;

Performance Metrics

Comprehensively measure latency, throughput, memory usage, power consumption, accuracy loss, and cold start time;

Hardware Platforms

Covers high-end GPUs, mid-range GPUs, integrated graphics, and ARM processors, making the conclusions practically instructive.

Conclusion: Key Findings and Engineering Insights

1. Quantization balance: INT8 provides significant improvements but may lose accuracy; mixed precision strategy is recommended;
2. Hardware awareness: Optimal configurations vary across hardware (e.g., FP16 for NVIDIA GPUs, INT8 for Intel CPUs);
3. ONNX usage: Targeted optimizations (graph optimization, execution configuration) are needed to unlock potential;
4. Batching strategy: Dynamic batching balances throughput and latency.

Recommendations: Practical Guide and Extension Directions

Reproduction Path

1. Environment preparation: Specific versions of PyTorch, ONNX Runtime, quantization tools, etc.;
2. Step-by-step process: Baseline establishment → FP16 conversion → INT8 quantization → ONNX export → Runtime tuning;

Extension Directions

Optimization of larger models, quantization of generative models, multi-modal inference optimization, dynamic optimization for continuous learning.