llm-inference-bench: A vLLM-based Inference Performance Benchmarking Framework for Large Language Models

This article introduces the open-source framework llm-inference-bench, built on vLLM, which focuses on systematic benchmarking of large language model inference performance. The framework supports multiple quantization formats (FP16/INT8/INT4), batch size configurations, and covers mainstream models (e.g., Mistral 7B, Llama3.1 8B). It evaluates performance across dimensions such as throughput, latency percentiles, and memory efficiency, providing a data-driven basis for model deployment decisions.

In the actual deployment of LLMs, inference performance is key to user experience and cost-effectiveness. As a vLLM-based benchmarking framework, llm-inference-bench aims to provide a standardized performance evaluation method, focusing on quantitative analysis of model performance in real inference scenarios to help developers make informed technical choices before deployment.

The framework comprehensively evaluates models from three dimensions:

1. Throughput: Measures the number of requests processed per unit time, simulating real loads to test carrying capacity under different configurations;
2. Latency percentiles: Uses P50/P90/P99 analysis to present response time distribution, helping identify performance bottlenecks;
3. Memory efficiency: Records VRAM usage under different configurations to support hardware selection.

In terms of quantization formats, it supports FP16 (original precision), INT8 (balance between precision and efficiency), and INT4 (extreme compression), helping developers compare quantization gains and precision loss; models cover mainstream open-source ones such as Mistral7B (with efficient attention mechanism) and Llama3.1 8B (Meta's latest generation with excellent performance), ensuring wide reference value of evaluation results.

Batching is a key technology to improve inference efficiency. The framework supports testing different batch sizes to help users find the optimal strategy—too large a batch may increase latency, while too small fails to fully utilize hardware resources.

For deployment teams, the value of this framework includes:

1. Technical selection reference: Choose models and quantization schemes based on measured data;
2. Capacity planning: Estimate required hardware resources;
3. Optimization verification: Compare performance before and after deployment to validate optimization effects;
4. Cost control: Select cost-effective configurations within acceptable precision ranges.

As LLM applications deepen, inference performance optimization becomes increasingly important. With systematic evaluation methods and rich configurations, llm-inference-bench provides a valuable open-source tool for this field. Whether researchers are exploring efficiency boundaries or engineers are planning production deployments, it is worth referencing.