Local Large Language Model Inference Benchmarking System: Comprehensive Evaluation of Your AI Performance

The Local Large Language Model Inference Benchmarking System (Local-LLM-Inference-Benchmarking-System) is an open-source tool developed by vectorvoyager358 and released on GitHub on May 30, 2026. This system aims to help developers and researchers objectively evaluate the inference performance of large language models in local environments, supporting comparisons of the performance of different models, hardware configurations, and inference frameworks. Its core value lies in providing standardized testing methods and multi-dimensional metrics, offering data support for local deployment decisions (such as hardware selection and framework choice).

Local LLM deployment faces the complexity of performance evaluation: it needs to consider multi-dimensional metrics such as accuracy, inference speed, memory usage, power consumption, and concurrency capability. Different scenarios have significantly different requirements—real-time dialogue focuses on first-token latency, batch processing tasks value throughput, and mobile devices need to balance performance and battery life. In addition, parameters like quantization precision and batch size significantly affect results, and the lack of standardized testing makes fair comparison difficult. This system eliminates variables through a unified framework and provides repeatable, comparable results.

Modular Design

The system adopts a modular architecture, including a model loader (supports multiple formats/backends), a test case generator (automatically generates standardized inputs), a performance monitor (collects metrics in real time), and a result analyzer (statistics and visualization), with strong scalability.

Multi-dimensional Metrics

• Latency: Time to First Token (TTFT), Time per Token (TPOT), end-to-end latency
• Throughput: Token generation rate, request processing capability, concurrency performance
• Resources: Memory usage, GPU utilization, power consumption
• Quality: Output consistency, long text processing capability

Flexible Configuration

Supports custom model parameters (quantization precision, context length), hardware configurations (GPU/CPU restrictions), test loads (single request/concurrency), and input data (standard/custom test cases).

1. Hardware Selection: Compare the performance of different hardware for target models (e.g., cost-effectiveness of consumer-grade GPUs for 7B models, multi-card solutions for 70B models).
2. Framework Comparison: Evaluate performance differences and optimization technology support of frameworks like llama.cpp and vLLM under the same conditions.
3. Model Optimization Verification: Compare performance changes before and after optimization, and evaluate the impact of quantization on speed/accuracy.
4. CI/CD Integration: Automated performance regression testing, monitoring online service baselines, and detecting performance degradation issues.

• Precise Timing: Use high-precision timers, exclude cold start effects, and take the average of multiple runs.
• Resource Isolation: Set process affinity, GPU computing mode, and clean up background tasks to ensure repeatable results.
• Result Presentation: Provide visualizations such as line charts/bar charts, support CSV/JSON/HTML export, and historical trend analysis.

Community Contribution

We welcome participation in forms such as test data sharing, new hardware support, test case expansion, and documentation improvement, with the goal of building a comprehensive local LLM performance database.

Getting Started Steps

1. Environment Preparation: Install Python, CUDA (if using NVIDIA GPU), and the target inference framework.
2. Model Acquisition: Download model files from platforms like Hugging Face/ModelScope.
3. Test Configuration: Edit the configuration file to specify the model path, parameters, and output options.
4. Execute Test: Run the main program and wait for completion.
5. View Results: Analyze the report to compare the performance of different configurations.

Current Limitations: Limited multi-modal support, insufficient distributed testing capabilities, and lack of coverage for real-time streaming scenarios. Future Plans: Gradually solve the above problems and synchronize with the latest model and technology updates.

The Local-LLM-Inference-Benchmarking-System provides a key evaluation tool for local LLM deployment. Against the backdrop of rapid technological iteration, objective performance data is crucial for decision-making. With the growth of the community and the improvement of functions, this system is expected to become a standard benchmarking platform in the local LLM field.