Empirical Study on Algorithm-Hardware Co-Design for Large Language Model Inference

Core Overview

This study conducts an empirical analysis of large language model (LLM) inference on consumer-grade GPU platforms, systematically evaluating the impact of low-precision quantization and structured sparsity techniques on inference throughput, memory utilization, power consumption, and model quality, and explores the key role of algorithm-hardware co-design in the efficient deployment of LLMs.

Keywords: Large Language Model, Inference Optimization, Quantization, Sparsification, GPU, Algorithm-Hardware Co-Design, AWQ, Deep Learning, Model Compression

Original Author/Source: lwamzeche (GitHub) | Publication Time: June 9, 2026 | Original Link: https://github.com/lwamzeche/Algorithm-Hardware-Co-Design

Research Background and Motivation

In the field of AI computing, the exponential growth of hardware performance is the core driver of technological progress. NVIDIA CEO Jensen Huang pointed out that while Moore's Law has improved computing performance by about 100x over the past decade, the 'extreme co-design' combining model, software stack, and hardware architecture has achieved an improvement of about 1 million times, highlighting the key role of co-design.

As the scale of LLMs continues to expand, efficient deployment on resource-constrained hardware has become an engineering challenge. Traditional single optimization strategies struggle to balance performance, efficiency, and model quality, and co-design provides a systematic solution.

Research Objectives and Methods

Core Questions

• How do low-precision quantization techniques affect inference performance and model quality?
• Can structured sparsity reduce computational overhead while maintaining model capabilities?
• How do different hardware platform characteristics affect the effectiveness of optimization strategies?

Experimental Setup

• Evaluation Models: Llama 3.1 8B (main model), Llama 3.2 1B, Qwen 1.5-1.8B (cross-model validation)
• Hardware Platforms: NVIDIA T4, L4, A100 (covering GPUs of different positioning)

Key Technology Analysis

Low-Precision Quantization Techniques

• BitsAndBytes INT8/INT4 Quantization: Post-training quantization, compressing FP32/FP16 weights into 8/4-bit integers, reducing model size and memory bandwidth requirements; INT4 has higher compression ratio but may introduce precision loss.
• AWQ (Activation-Aware Weight Quantization): Activation-aware weight quantization, which differentially processes weights based on the importance of activation distribution, maintaining better model quality at low bits.

Structured Sparsity Techniques

• Naive 2:4 Structured Pruning: Retain 2 out of every 4 consecutive weights, accelerated using sparse tensor cores of NVIDIA Ampere and newer architectures.
• 2:4 Sparse Mask Generated by MaskLLM: Learned mask generation, intelligently retaining key weights, which is better than random/magnitude pruning.

Experimental Design and Evaluation Dimensions

The study comprehensively evaluates the optimization effects from five dimensions:

1. Inference Throughput: Number of tokens processed per unit time, affecting user experience and concurrency capability
2. Memory Utilization: GPU memory usage, determining the scale of models that can be deployed on a single card
3. Power Consumption: GPU inference power consumption, related to operational costs
4. Energy Efficiency Ratio: Inference workload completed per watt, measuring the economic efficiency of the technology
5. Model Quality: Evaluate the impact of quantization/sparsity on model capabilities through perplexity and downstream task accuracy

Research Findings and Insights

• Quantization Effects: Low-precision quantization significantly improves throughput and reduces memory usage, with acceptable model quality loss; the AWQ INT4 scheme maintains good performance.
• Sparsity Effects: Structured sparsity depends on implementation and hardware support; the 2:4 mode brings substantial acceleration on GPUs supporting sparse tensor cores.
• Cross-Hardware Differences: T4 is sensitive to memory optimization; L4 has outstanding energy efficiency ratio; A100 has the strongest performance but limited optimization space. Deployers need to choose optimization combinations based on hardware characteristics.

Practical Significance and Application Recommendations

Guidance for engineers/researchers deploying LLMs in production environments:

• Quantization Strategy: Prioritize INT8 in memory-constrained scenarios; try AWQ INT4 under extreme constraints
• Sparsity Application: Enable structured sparsity only when the target hardware supports sparse tensor cores
• Hardware Selection: Choose T4/L4/A100 based on throughput requirements and power budget
• Quality Verification: Fully validate downstream tasks after optimization to ensure meeting business needs

Conclusion

As LLMs evolve toward larger scales and wider applications, algorithm-hardware co-design will become the core competitiveness of AI engineering. This study provides real-effect data of quantization and sparsity techniques, helping practitioners balance performance, cost, and model quality. In the future, advances in next-generation AI chips and model compression technologies will further leverage the key role of co-design.