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LLMEasyQuant: Scalable Quantization for Parallel and Distributed LLM Inference

Dong Liu, Yanxuan Yu

TL;DR

LLMEasyQuant addresses the growing need for scalable, transparent quantization of large language models by delivering a modular framework that unifies multiple backends (including SimQuant, SmoothQuant, ZeroQuant, AWQ, and GPTQ) with architecture-aware optimizations and fused CUDA kernels. The system supports static and online quantization, per-layer bitwidth search, and distributed synchronization, enabling near-linear multi-GPU scaling and significant memory savings across edge to cloud deployments. Comprehensive experiments on GPT-2, LLaMA, Mistral, and Qwen3-14B demonstrate competitive perplexity and throughput with reduced calibration data and setup time, highlighting practical deployment benefits. The accompanying theoretical analysis provides complexity bounds, convergence guarantees, and error propagation results, grounding the design choices and offering guidance for deployment across diverse hardware and deployment scenarios.

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 \textbf{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 speedup 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.

LLMEasyQuant: Scalable Quantization for Parallel and Distributed LLM Inference

TL;DR

LLMEasyQuant addresses the growing need for scalable, transparent quantization of large language models by delivering a modular framework that unifies multiple backends (including SimQuant, SmoothQuant, ZeroQuant, AWQ, and GPTQ) with architecture-aware optimizations and fused CUDA kernels. The system supports static and online quantization, per-layer bitwidth search, and distributed synchronization, enabling near-linear multi-GPU scaling and significant memory savings across edge to cloud deployments. Comprehensive experiments on GPT-2, LLaMA, Mistral, and Qwen3-14B demonstrate competitive perplexity and throughput with reduced calibration data and setup time, highlighting practical deployment benefits. The accompanying theoretical analysis provides complexity bounds, convergence guarantees, and error propagation results, grounding the design choices and offering guidance for deployment across diverse hardware and deployment scenarios.

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 \textbf{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 speedup 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.
Paper Structure (48 sections, 21 theorems, 45 equations, 2 figures, 2 tables)

This paper contains 48 sections, 21 theorems, 45 equations, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. System Design of LLMEasyQuant
  4. Architecture-Aware Optimization
  5. Workflow
  6. System Design
  7. Generalized Parallel Quantization Runtime
  8. Hardware-Specific Scheduling and Fusion
  9. Distributed Quantization Synchronization
  10. Runtime Adaptation and Fused Recalibration
  11. ONNX-Compatible Quantization Serialization
  12. Summary of System Design
  13. Experimental Results
  14. Model Coverage and Experimental Setup
  15. Comprehensive Perplexity Analysis Across Modern Models
  16. ...and 33 more sections

Key Result

theorem 1

For a weight matrix $W \in \mathbb{R}^{D \times D'}$ and activation tensor $X \in \mathbb{R}^{B \times D}$, the time complexity of quantization operations is $O(BD + DD')$ for per-tensor quantization and $O(BD + DD' \cdot D)$ for per-channel quantization, where $B$ is the batch size, $D$ is the feat

Figures (2)

  • Figure 1: Quantized Weights Distribution
  • Figure 2: Performance Comparison after Quantization on GPT

Theorems & Definitions (42)

  • theorem 1: Quantization Time Complexity
  • proof : Proof of Quantization Complexity
  • theorem 2: Quantized GEMM Complexity
  • proof : Proof of Quantized GEMM Complexity
  • theorem 3: Multi-GPU Quantization Complexity
  • proof : Proof of Distributed Complexity
  • lemma 1: Quantization Error Decomposition
  • proof : Proof of Lemma A.1
  • lemma 2: Bound on Quantization Operator
  • proof : Proof of Lemma A.2
  • ...and 32 more