L40S LLM Inference Benchmark Framework: A Reproducible Performance Evaluation Tool for OpenAI-Compatible Servers

This project is a reproducible LLM inference benchmark framework for NVIDIA L40S GPUs and OpenAI-compatible servers, maintained by lijiaweiphilip-web. The source code is hosted on GitHub (link: https://github.com/lijiaweiphilip-web/l40s-llm-bench), and it was released on June 1, 2026. Its core goal is to help developers and operation teams systematically evaluate the throughput, latency, and concurrency performance of inference services, providing quantitative basis for capacity planning and performance tuning in production environments.

Practical Challenges in LLM Inference Performance Evaluation

Evaluating the performance of large language model inference services is complex: there are trade-offs between latency, throughput, and concurrency; input/output sequence length variations have significant impacts; and it's hard to compare the effects of different hardware and optimization strategies. The lack of standardized tools leads to: difficulty in objectively comparing model/config differences, no reliable data for capacity planning, and difficulty in detecting performance regressions.

NVIDIA L40S GPU Features

The L40S is a GPU designed specifically for data center inference, based on the Ada Lovelace architecture. It has 48GB GDDR6 memory (capable of accommodating FP16 versions of mainstream LLMs), supports multi-precision Tensor Cores, NVLink multi-GPU interconnection, and a 350W TDP that balances performance and energy efficiency. Compared to the H100, it is more cost-effective in inference scenarios and suitable for medium-scale LLM deployments.

Architecture Design

The framework is designed around OpenAI-compatible APIs and supports backends such as vLLM, TensorRT-LLM, TGI, and self-developed inference services.

Testing Dimensions

1. Latency Testing: Time to First Token (TTFT), Inter-Token Latency (ITL), end-to-end latency;
2. Throughput Testing: Token throughput, request throughput, concurrency scalability curve;
3. Stress Testing: Maximum concurrency count, long-tail latency analysis, error rate/timeout rate statistics.

Configurable Parameters

Supports model parameters (name, maximum sequence length, etc.), request parameters (input/output length distribution, etc.), load parameters (concurrency count, request rate, etc.), and output parameters (result format, visualization options, etc.).

The core design concept of the project is reproducibility, with the following specific measures:

1. Deterministic Load Generation: Fixed random seeds are used to generate test requests, ensuring consistent inputs across multiple runs;
2. Environment Isolation: Docker containerized deployment to avoid external interference;
3. Result Standardization: Outputs standard JSON format, including test configuration, raw data, and statistical summaries;
4. Hardware Information Recording: Automatically captures GPU model, driver version, CUDA version, etc., to facilitate cross-environment comparison.

1. Model Selection Evaluation: Compare the performance of candidate models to support technical selection;
2. Optimization Strategy Validation: Quantify the benefits of techniques such as quantization and KV Cache optimization;
3. Capacity Planning: Simulate real loads to determine the minimum hardware configuration that meets SLAs;
4. Performance Monitoring and Regression Detection: Integrate into CI/CD pipelines to detect performance regressions in a timely manner.

Feature	l40s-llm-bench	vLLM benchmarks	llmperf
OpenAI API Compatibility	Yes	No	Yes
Multi-Backend Support	Yes	No (vLLM only)	Yes
Reproducibility Design	Strong	Medium	Medium
L40S-Specific Optimization	Yes	No	No
Report Visualization	Built-in	Basic	Basic

The advantages of this tool lie in its L40S-specific optimization and strong reproducibility design, making it suitable for strict comparison tests in production environments.

Limitations

The current version only focuses on single-node L40S evaluation and does not cover multi-node distributed scenarios; the tests use synthetic loads, which may differ from real production traffic.

Usage Recommendations

1. Combine with real logs: Integrate synthetic load testing with production log analysis to get a comprehensive performance profile;
2. Regular retesting: Updates to hardware drivers, CUDA versions, etc., may affect performance, so regular retesting is recommended;
3. Multi-dimensional comparison: Pay attention to tail latency and outliers, as these determine user experience.

l40s-llm-bench provides a practical and reliable tool for evaluating the performance of LLM inference services. Through standardized testing processes, reproducible load generation, and rich metric outputs, it helps teams establish objective performance baselines, supporting optimization decisions and capacity planning. For teams deploying LLM services using L40S, it is a benchmark framework worth adding to their toolbox.