LLM Inference Benchmark Lab: Reproducible Local Hardware Inference Optimization Solutions

This article introduces the open-source llm-inference-benchmark project developed by Happynood, an LLM inference optimization benchmark lab tailored for local hardware deployment scenarios. The project aims to help developers systematically compare latency, VRAM usage, and output quality across different inference backends and quantization schemes through reproducible testing workflows, providing data support for LLM inference optimization.

LLM inference performance optimization is a core challenge in AI engineering, requiring trade-offs between inference speed, VRAM usage, output quality, and hardware costs. Influencing factors include model architecture, quantization precision, inference backends, hardware configurations, etc. Minor configuration changes can lead to significant performance differences, hence the need for systematic benchmarking tools.

The core design goals of llm-inference-benchmark include:

1. Reproducibility: Ensure consistent results through standardized workflows, fixed random seeds, and environment dependency declarations;
2. Multi-dimensional Comparison: Cover backend efficiency, quantization impact, resource consumption, and output quality;
3. Local Hardware Focus: Optimized for the VRAM limitations and computing characteristics of consumer GPUs, supporting evaluation by individual developers and small-to-medium teams.

The project's tests cover multiple technical dimensions:

• Inference Backend Comparison: Supports mainstream backends like llama.cpp, vLLM, TensorRT-LLM, ExLlamaV2, AutoGPTQ/AutoAWQ, etc.;
• Quantization Scheme Evaluation: Compares precision from FP16 to INT4, GPTQ/AWQ/GGUF algorithms, grouping strategies, and mixed-precision schemes;
• Latency & Throughput Analysis: Measures first-token latency, per-token latency, end-to-end latency, and throughput;
• VRAM Monitoring: Tracks peak VRAM usage, growth patterns, KV cache efficiency, and multi-model concurrency;
• Output Quality Validation: Ensures quality through consistency checks, benchmark datasets, human evaluation support, and anomaly detection.

The project's practical value includes:

• Hardware Selection Decision: Quantify model performance on different GPUs to assist return-on-investment (ROI) analysis;
• Deployment Configuration Optimization: Identify optimal backends, quantization levels, and batch sizes;
• Model Selection Reference: Understand the performance of specific models after quantization;
• Performance Regression Detection: Integrate into CI workflows to detect performance degradation caused by code or configuration changes.

The project's technical implementation features:

• Modular Architecture: Divided into driver layer (backend adaptation), measurement layer (metric collection), analysis layer (result processing), and report layer (report generation);
• Configuration-Driven: Define test matrices (model, backend, quantization scheme, benchmark type) via YAML configuration files;
• Result Visualization: Provide interactive charts to display comparison results, enabling intuitive understanding of performance differences.

The project has the following limitations:

• Hardware Specificity: Test results are affected by GPU model, driver version, and system configuration; cross-hardware comparisons require caution;
• Model Coverage: Dependent on community contributions, may not keep up with the latest models in a timely manner;
• Workload Representativeness: Synthetic tests may not fully match real application scenarios; it is recommended to verify with actual data.

The llm-inference-benchmark fills a gap in the field of LLM inference optimization, providing a neutral, open, and reproducible evaluation platform that is of great value for maintaining ecosystem health and technical transparency. It offers a systematic learning tool for developers and researchers, helping them make informed optimization decisions. As LLM applications expand, such benchmarking tools will play an increasingly important role in performance engineering.