# LLM Inference Bench: Cross-Platform LLM Inference Performance Benchmark Tool

> A platform-agnostic benchmark framework for large language model (LLM) inference endpoints, supporting the measurement of metrics like TTFT, throughput, and failure rate, and compatible with OpenAI-compatible APIs such as vLLM and SGLang.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-06-02T00:16:19.000Z
- 最近活动: 2026-06-02T00:25:34.346Z
- 热度: 159.8
- 关键词: LLM, inference, benchmark, vLLM, SGLang, TTFT, throughput, 性能测试
- 页面链接: https://www.zingnex.cn/en/forum/thread/llm-inference-bench-3eae199d
- Canonical: https://www.zingnex.cn/forum/thread/llm-inference-bench-3eae199d
- Markdown 来源: floors_fallback

---

## LLM Inference Bench: Cross-Platform LLM Inference Performance Benchmark Tool

LLM Inference Bench is a platform-agnostic benchmark framework for LLM inference endpoints. It supports OpenAI-compatible APIs (e.g., vLLM, SGLang, TensorRT-LLM) and measures core metrics like TTFT, throughput, and failure rate. Key features include data-driven configuration recommendations, production scenario simulation, and easy-to-use CLI. It helps with inference engine selection, hardware procurement, parameter tuning, capacity planning, and performance regression testing.

## Background: Pain Points in LLM Inference Performance Evaluation

As LLMs move to production, organizations face challenges in objectively evaluating the performance of inference solutions. Existing issues: 
1. Vendor self-test data is idealized and not realistic.
2. Manual random tests lack statistical significance.
3. Focus on single metrics (e.g., only throughput) ignores trade-offs.
4. Test tools are platform-locked, hindering cross-solution comparison.
5. Parameter tuning relies on experience rather than data. These call for a standardized, cross-platform, multi-dimensional benchmark tool.

## Core Positioning & Key Features

LLM Inference Bench is designed to solve the above pain points. Its core positioning: platform-agnostic benchmark framework for LLM inference endpoints. 
Design goals: cross-platform compatibility, multi-dimensional measurement, data-driven config, production scenario simulation. 
Core features: 
- Supports OpenAI-compatible APIs (vLLM, SGLang, etc.).
- Measures TTFT, throughput, failure rate.
- Provides vLLM configuration recommendations.
- Easy-to-use CLI interface.

## Key Performance Metrics

The tool measures three core metrics:
1. **TTFT**: Time from request to first token, affecting user experience. Factors: model loading, input preprocessing, network delay. Optimization: prefix caching, tokenization speed.
2. **Throughput**: Tokens processed per second (output, total, request throughput). Factors: GPU capacity, batch efficiency, concurrency.
3. **Failure Rate**: Proportion of failed requests (timeout, OOM, connection errors). Critical for production reliability.

## Cross-Platform Compatibility Design

The tool achieves cross-platform support via:
1. **OpenAI-compatible API**: Uses /v1/completions endpoint (supported by vLLM, SGLang, TensorRT-LLM, Baseten, RHOAI, etc.).
2. **Unified measurement**: Same request format, timing method, and stats calculation across platforms.
3. **Config abstraction**: Users only need to provide API URL, auth info, model name; tool handles platform-specific details.

## Test Scenarios for Realistic Simulation

To mimic production environments, the tool supports:
1. **Concurrent pressure test**: Simulate multiple users with configurable concurrency, total requests, and arrival mode.
2. **Variable input/output tests**: Test performance with different input/output lengths to evaluate KV cache efficiency and stability.
3. **Mixed workload**: Combine short/long input/output tasks (e.g., simple QA, summary, creation) to reflect real usage.

## Data-Driven Configuration Recommendations

The tool provides vLLM configuration recommendations based on actual measurement data:
- **Tensor Parallelism**: Recommend parallelism based on GPU count and model size.
- **Batch Size**: Balance delay and throughput considering memory limits.
- **Scheduling Strategy**: Optimize continuous batching for GPU utilization.
- **KV Cache**: Recommend cache size and eviction policy. Recommendations are validated against hardware constraints and vLLM's internal features.

## Conclusion & Value

LLM Inference Bench fills a gap in LLM inference performance evaluation. Its value:
- **Ops teams**: Objective assessment, bottleneck identification, capacity planning.
- **Dev teams**: Optimization guidance, regression protection.
- **Decision-makers**: Data-driven selection of solutions, ROI evaluation. Limitations: config recommendations focus on vLLM; test data may not fully represent real workloads. Usage tips: use real data for calibration, run multiple tests, combine with production monitoring.