Deploying Quantized LLMs for Efficient Inference
Chapter 1: Advanced LLM Quantization Fundamentals
Revisiting Quantization Principles for Large Models
Low-Bit Quantization Techniques (Below INT8)
Understanding Quantization Data Types and Formats
Post-Training Quantization (PTQ) Algorithms for LLMs
Quantization-Aware Training (QAT) Considerations
Mixed-Precision Quantization Strategies
Calibration Data Selection and Preparation
Hands-on Practical: Applying GPTQ to an LLM
Chapter 2: Implementing LLM Quantization with Toolkits
Overview of LLM Quantization Libraries
Using bitsandbytes for Low-Bit Operations
Quantization with Hugging Face Transformers and Accelerate
Applying GPTQ using AutoGPTQ
Applying AWQ using AutoAWQ
Comparing Toolkit Outputs and Performance
Handling Model Compatibility Issues
Practice: Quantizing Models with Multiple Toolkits
Chapter 3: Performance Evaluation of Quantized LLMs
Metrics for Quantized Model Evaluation
Measuring Inference Latency and Throughput
Assessing Memory Consumption (Disk and Runtime)
Evaluating Accuracy Degradation
Benchmarking Frameworks and Tools
Analyzing Performance on Target Hardware
Visualizing Performance Trade-offs
Hands-on Practical: Benchmarking a Quantized LLM
Chapter 4: Optimizing and Deploying Quantized LLMs
Inference Optimization Techniques Post-Quantization
Choosing the Right Deployment Framework
Deploying with Text Generation Inference (TGI)
Leveraging vLLM for High-Throughput Inference
GPU Optimization with NVIDIA TensorRT-LLM
Deployment using ONNX Runtime
Containerization and Scaling Strategies
Monitoring Deployed Quantized Models
Hands-on Practical: Deploying via an Inference Server
Chapter 5: Addressing Advanced Challenges
Mitigating Accuracy Loss in Low-Bit Regimes
Handling Activation and Weight Outliers
Quantizing Specific LLM Components (Attention, Normalization)
Hardware Constraints and Kernel Availability
Dynamic Quantization vs. Static Quantization Trade-offs
Debugging Quantization Issues
Integrating Quantized Models into Production Pipelines
Practice: Fine-tuning Quantization Parameters
Quantizing Specific LLM Components (Attention, Normalization)
Was this section helpful?
- LLM.int8(): 8-bit Matrix Multiplication for Large Language Models, Tim Dettmers, Mike Lewis, Younes Belkada, Luke Zettlemoyer, 2022 NeurIPS 2022 DOI: 10.48550/arXiv.2208.07339 - Introduces a mixed-precision approach for 8-bit quantization in LLMs, specifically addressing the outlier problem that causes high dynamic range, a key challenge for attention scores.
- Q-BERT: Quantizing BERT for Fast Inference, Sheng Shen, Zhenglun Ma, Kai Hou, Ruohui Ye, Zizheng Niu, Wenshuo Li, Feng Wu, Amir Gholami, Shunning Wei, Michael W. Mahoney, and Kurt Keutzer, 2019 Advances in Neural Information Processing Systems 32 (NeurIPS 2019) (NeurIPS) DOI: 10.48550/arXiv.1909.05846 - A foundational work that details challenges in quantizing Transformer models, including the sensitivity of softmax and normalization layers, and proposes solutions for maintaining accuracy.