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
Assessing Memory Consumption (Disk and Runtime)
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- LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale, Tim Dettmers, Mike Lewis, Younes Belkada, Luke Zettlemoyer, 2022 NeurIPS 2022 DOI: 10.48550/arXiv.2208.07339 - This paper introduces a widely adopted 8-bit quantization method for large language models, reducing memory footprint while maintaining performance.
- CUDA semantics, PyTorch Documentation Team, 2016 (PyTorch) - Official documentation on PyTorch's CUDA memory management functions, including
memory_allocatedandmemory_reserved, directly referenced in the section. - NVIDIA System Management Interface (nvidia-smi), NVIDIA Corporation, 2024 (NVIDIA) - Official resource for the
nvidia-smicommand-line utility, a tool for monitoring GPU memory usage discussed in the section. - Quantization - Hugging Face Transformers, Hugging Face, 2024 (Hugging Face) - Provides practical guidance and implementations for various quantization techniques (including 8-bit and 4-bit) used in deploying LLMs, impacting disk and runtime memory.
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