This title slide is titled "Quantization and Compression Techniques for Efficient Large Language Models." Its subtitle explores mathematical quantization principles and algorithmic advances for efficient LLMs, from theory to deployment.

Quantization and Compression Techniques for Efficient Large Language Models

Exploring mathematical quantization principles and algorithmic advances for efficient LLMs, from theory to deployment.

This slide introduces the rise of large language models (LLMs) and their computational challenges, exemplified by a 70B parameter model requiring 140GB in FP16 precision. The core motivation is to improve efficiency without sacrificing accuracy.

Introduction

• Rise of large language models (LLMs) and computational challenges
• 70B parameter model requires 140GB in FP16 precision
• Motivation: Efficiency without sacrificing accuracy

Source: Quantization and Compression Techniques for Efficient Large Language Models

The slide outlines the mathematical foundations of quantization via the affine mapping formula x̂ = s · round(x/s + z), which maps continuous values to discrete levels. It highlights the statistical bias-variance trade-off, balancing compression gains against accuracy loss by minimizing error through scaling and zero-point.

Mathematical Foundations of Quantization

• Affine mapping: x̂ = s · round(x/s + z)
• Statistical view: bias-variance trade-off
• Balances compression against accuracy loss
• Maps continuous values to discrete levels
• Minimizes quantization error via scaling and zero-point

This section header slide introduces Section 03: "QSGD – Communication-Efficient SGD" within the topic of quantization and compression for efficient large language models. It highlights quantizing gradients in distributed training to maintain unbiasedness and provable convergence bounds.

Quantization and Compression Techniques for Efficient Large Language Models

03

QSGD – Communication-Efficient SGD

Quantize gradients for distributed training while preserving unbiasedness with provable convergence bounds.

Source: Dan Alistarh et al. (NeurIPS 2017)

GPTQ enables post-training quantization of models without retraining. It uses layer-wise least-squares optimization to minimize per-layer quantization error, achieving near full-precision accuracy.

Post-Training Quantization – GPTQ

• Quantizes trained models without retraining.
• Uses layer-wise least-squares optimization.
• Minimizes quantization error per layer.
• Achieves near full-precision accuracy.

Source: Frantar et al., ICLR 2023

SmoothQuant tackles activation outliers that degrade quantization performance by jointly scaling activations and weights before quantization. This smooths activation distributions for stability, reduces quantization loss via simple rescaling, and enables effective low-bit LLM deployment.

SmoothQuant – Activation and Weight Balancing

• Activation outliers degrade quantization performance
• Jointly scale activations and weights pre-quantization
• Smooths activation distributions for stability
• Reduces quantization loss via simple rescaling
• Enables effective low-bit LLM deployment

Source: Xiao et al., SmoothQuant (NeurIPS 2022)

AWQ (Activation-Aware Weight Quantization) leverages activation statistics, layer sensitivity, and real LLM distributions for precise, model-aware 4-bit weight quantization with minimal accuracy loss. It delivers strong performance on OPT and LLaMA models while enhancing SmoothQuant for superior low-bit results.

AWQ – Activation-Aware Weight Quantization

{ "features": [ { "icon": "📊", "heading": "Activation Stats Guide", "description": "Leverages activation statistics to inform precise weight quantization." }, { "icon": "🎯", "heading": "Layer Sensitivity Aware", "description": "Handles varying layer importance for minimal accuracy loss." }, { "icon": "⚡", "heading": "4-Bit Excellence", "description": "Delivers strong performance on OPT and LLaMA models." }, { "icon": "🧠", "heading": "Model-Aware Approach", "description": "Tailors quantization using real LLM activation distributions." }, { "icon": "🔄", "heading": "Enhances SmoothQuant", "description": "Builds on smoothing for superior low-bit quantization." } ] }

Source: Lin et al., AWQ (arXiv 2023)

QLoRA finetunes 4-bit quantized models using LoRA adapters while freezing weights to save memory. It delivers high accuracy with low resources, enabling efficient adaptation of large LLMs.

QLoRA – Finetuning Quantized Models

• Finetunes 4-bit models using LoRA adapters.
• Freezes quantized weights to save memory.
• Achieves high accuracy with low resources.
• Enables efficient adaptation of large LLMs.

Source: Tim Dettmers et al., ICML 2023

The slide on System-Level Optimization highlights hardware acceleration using NVIDIA TensorRT and Intel AMX for hardware-aware quantization and mixed precision (FP16/INT8), reducing memory/compute costs while preserving accuracy for fast LLM inference. It also covers distributed training efficiency via quantized gradients (e.g., QSGD), which cut communication overhead to enable scalable GPU cluster training with minimal bandwidth and faster convergence.

System-Level Optimization

Source: Quantization and Compression Techniques for Efficient Large Language Models

The "Mathematical Throughline" slide outlines affine mappings as the core structure, with scaling to balance dynamic ranges. It emphasizes error minimization via MSE and KL divergence, unifying theory with implementation.

Mathematical Throughline

• Affine mappings as core structure
• Scaling balances dynamic ranges
• Error minimization via MSE, KL
• Unifies theory to implementation

Source: Quantization and Compression Techniques for Efficient Large Language Models

This conclusion slide summarizes how quantization enables LLM deployment and highlights key math and engineering trends. It outlines a future of adaptive, error-bounded precision, ending with "Thank you!"

Summary and Outlook

• Quantization enables LLM deployment

• Math + engineering trends
• Future: Adaptive, error-bounded precision

Thank you!

Source: Quantization and Compression Techniques for Efficient Large Language Models

The "References" slide features a table listing five key papers on model quantization techniques, including their lead authors. The entries are QSGD (Alistarh, NeurIPS 2017), GPTQ (Frantar, ICLR 2023), SmoothQuant (Xiao, NeurIPS 2022), AWQ (Lin, arXiv 2023), and QLoRA (Dettmers, ICML 2023).

References

{ "headers": [ "Paper", "Venue/Year" ], "rows": [ [ "QSGD (Alistarh)", "NeurIPS 2017" ], [ "GPTQ (Frantar)", "ICLR 2023" ], [ "SmoothQuant (Xiao)", "NeurIPS 2022" ], [ "AWQ (Lin)", "arXiv 2023" ], [ "QLoRA (Dettmers)", "ICML 2023" ] ] }

Source: Quantization and Compression Techniques for Efficient Large Language Models