local-inference-bench: A Toolkit for Local Large Model Inference Performance Benchmarking

local-inference-bench is an open-source tool focused on inference performance benchmarking for local large language models (LLMs). It aims to help developers systematically evaluate and compare the inference efficiency and resource consumption of different models in local hardware environments. It fills a gap in the local LLM deployment toolchain, and through standardized, reproducible benchmarking capabilities, it helps developers make more informed technical decisions and optimize resource utilization efficiency.

With the popularization of large language model technology, more and more developers and enterprises choose to deploy LLMs locally to meet data privacy, cost control, and customization needs. However, local deployment faces core challenges: how to select the optimal model configuration under limited hardware resources? Different model architectures, parameter scales, quantization precision, and inference frameworks have huge performance differences on the same hardware. The lack of systematic benchmarking tools leads developers to rely on experience or scattered information for decision-making, which easily causes resource waste or insufficient performance.

local-inference-bench is an open-source toolkit focused on inference performance benchmarking for local LLMs. It provides standardized benchmarking processes and metrics to help users comprehensively evaluate model inference performance. Its design philosophy is simplicity and practicality: it can quickly run benchmark tests and generate clear reports without complex configurations. Whether comparing the efficiency of different models or optimizing inference configurations for specific scenarios, it can provide valuable reference data.

local-inference-bench's core benchmarking dimensions include:

1. Throughput and Latency: Measures token generation speed and end-to-end latency under different input/output lengths, which directly affects user experience;
2. Memory Usage Analysis: Records peak and average memory usage during model loading and inference, which is crucial for edge device deployment;
3. CPU/GPU Utilization: Monitors hardware resource usage efficiency and helps identify performance bottlenecks (e.g., low GPU utilization may require optimizing batch size or parallelism);
4. Power Consumption and Efficiency: Provides power consumption-related metrics and calculates energy efficiency per token, suitable for data center and edge deployment scenarios.

local-inference-bench is designed to be framework-agnostic and supports mainstream local inference solutions:

• llama.cpp: A widely used C++ implementation that supports multiple quantization formats;
• Ollama: A user-friendly local model runtime environment;
• vLLM: A high-throughput production-grade inference engine;
• Transformers: Hugging Face's native PyTorch implementation. Multi-framework support allows users to compare the actual performance of different technical solutions under consistent standards.

The practical value of local-inference-bench is reflected in three major scenarios:

1. Hardware Selection Decision: Before purchasing new hardware, establish a performance baseline on existing devices to accurately evaluate the return on investment of new hardware;
2. Model Optimization Verification: When optimizing models (such as quantization and pruning), provide objective metrics to verify the effect, ensuring efficiency improvement without sacrificing quality;
3. Production Environment Configuration Tuning: Through systematic parameter scanning, find the configuration combination (e.g., batch size, number of threads, KV cache strategy) suitable for hardware and load characteristics.

local-inference-bench adopts a statistically robust benchmarking method: multiple runs to eliminate random fluctuations, and a warm-up mechanism to ensure measuring steady-state performance rather than cold-start overhead. When comparing across models, it is recommended to keep test conditions consistent: same hardware environment, similar input distribution, and sufficient test sample size. The tool's configuration file system supports saving and reproducing test settings.

As an open-source project, local-inference-bench welcomes community contributions (submitting new benchmarking scenarios, adding framework support, sharing test results). Maintainers also operate a public test result database for users to reference. Comparison with other tools:

• Compared to academic-oriented comprehensive benchmarks, it focuses more on practical deployment scenarios and only pays attention to inference performance rather than model capabilities;
• Compared to vendor-specific tools, it maintains neutrality and does not favor specific hardware or software stacks, ensuring objective and comparable results.