LLMEasyQuant: Scalable Quantization for Parallel and Distributed LLM Inference

2024-06-28

AI Summary

Existing LLM quantization tools lack transparency, flexibility, and system-level scalability in GPU and distributed environments, limiting memory compression and inference acceleration. This paper proposes a modular, system-aware low-bit quantization framework supporting single-node multi-GPU, multi-node, and edge-device deployments. It introduces the first unified interface integrating multiple quantization strategies—Symmetric, ZeroQuant, SmoothQuant, and SimQuant—with per-layer calibration, dynamic bit-width allocation, and runtime adaptivity. The framework synergistically combines CUDA kernel optimization, NCCL-based synchronization, and hybrid static/online quantization for hardware-software co-design. Experiments demonstrate substantial GEMM throughput improvement, higher HBM bandwidth utilization, near-linear multi-GPU scalability, and significant memory reduction and end-to-end latency decrease—all while preserving high model accuracy.

Technology Category

• Machine Learning: Hardware-aware ML
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

As large language models (LLMs) grow in size and deployment scale, quantization has become an essential technique for reducing memory footprint and improving inference efficiency. However, existing quantization toolkits often lack transparency, flexibility, and system-level scalability across GPUs and distributed environments. We present extbf{LLMEasyQuant}, a modular, system-aware quantization framework designed for efficient, low-bit inference of LLMs on single-node multi-GPU, multi-node, and edge hardware. LLMEasyQuant supports a wide range of quantization methods -- including Symmetric Quantization, ZeroQuant, SmoothQuant, and SimQuant -- with unified interfaces for per-layer calibration, bitwidth assignment, and runtime adaptation. It integrates fused CUDA kernels with NCCL-based distributed synchronization and supports both static and online quantization. Empirical results show that LLMEasyQuant can achieve substantial speedups in GEMM execution, HBM load time, and near-linear multi-GPU scaling. Ablation studies further validate its ability to balance latency, memory, and accuracy under diverse deployment conditions. LLMEasyQuant offers a practical quantization serving system for scalable, hardware-optimized LLM inference.

Problem

• Lack of transparent and flexible quantization toolkits for LLMs
• Insufficient system-level scalability across GPUs and distributed environments
• Need for efficient low-bit LLM inference on diverse hardware

Innovation

• Modular system-aware quantization framework
• Unified interfaces for diverse quantization methods
• Fused CUDA kernels with distributed synchronization