# llm-inference-benchmarks: A Benchmark Toolset for LLM Inference Performance

> An open-source LLM inference benchmark repository that provides a standardized testing framework and tools to evaluate the performance of different models, hardware configurations, and inference engines.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-30T04:42:40.000Z
- 最近活动: 2026-04-30T04:51:21.408Z
- 热度: 145.9
- 关键词: LLM推理, 基准测试, 性能评估, vLLM, TensorRT-LLM, 吞吐量, 延迟优化, GPU推理, 模型选型, 容量规划
- 页面链接: https://www.zingnex.cn/en/forum/thread/llm-inference-benchmarks
- Canonical: https://www.zingnex.cn/forum/thread/llm-inference-benchmarks
- Markdown 来源: floors_fallback

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## [Introduction] llm-inference-benchmarks: Core Introduction to the LLM Inference Performance Benchmark Toolset

This is an open-source project focused on evaluating the inference performance of large language models (LLMs). It provides a standardized testing framework and tools to assess the performance of different models, hardware configurations, and inference engines. Its core value lies in helping developers objectively compare inference performance under different configurations, providing data support for model selection, hardware procurement, engine optimization, and capacity planning, and promoting reproducible research in the field of LLM inference optimization.

## Why Do We Need LLM Inference Benchmarks?

LLM inference performance is affected by multiple factors: model architecture (Transformer variants, MoE architecture, quantization strategies), hardware platforms (GPU models, VRAM capacity, CPU/GPU collaboration), inference engines (vLLM, TensorRT-LLM, llama.cpp, TGI, etc.), and optimization techniques (KV Cache management, Continuous Batching, Speculative Decoding). Without a unified benchmark, performance comparisons often become invalid "apple-to-orange" comparisons.

## Typical Testing Dimensions: Comprehensive Evaluation of Inference Performance

The toolset covers the following core testing dimensions:
### Throughput Testing
Measures the number of tokens or requests processed per unit time. Key metrics include tokens per second (tok/s), total throughput (req/s), and Time To First Token (TTFT).
### Latency Testing
Focuses on single-request response speed, including end-to-end latency, per-token latency, and P50/P99 percentiles.
### Resource Utilization
Monitors hardware consumption: VRAM usage, GPU utilization, power consumption, and energy efficiency.
### Accuracy Comparison
Verifies the perplexity changes of quantized models and the accuracy of downstream tasks.

## Scientific Testing Methodology: Ensuring Reliable and Comparable Results

High-quality benchmarking follows four key principles:
1. **Standardized Input**: Use representative datasets (ShareGPT, LongBench, synthetic loads).
2. **Warm-up and Stabilization**: Eliminate interference from cold starts and cache misses.
3. **Multiple Sampling**: Repeat tests and report statistical distributions.
4. **Controlled Variables**: Change only one variable (model/engine/hardware) at a time to ensure comparability.

## Engineering Practice Value: Supporting Key Decision-Making Scenarios

This toolset has direct value in the following scenarios:
- **Model Selection**: Objectively compare the throughput and latency of models like Qwen2.5-72B and Llama-3.1-70B.
- **Hardware Procurement**: Evaluate the cost-effectiveness of A100 vs H100 vs RTX4090.
- **Engine Optimization**: Compare the optimization effects of vLLM's PagedAttention and TensorRT-LLM.
- **Capacity Planning**: Derive the required GPU quantity configuration based on target QPS and latency SLA.

## Ecological Significance: Promoting Standardization of LLM Inference Optimization

The emergence of the llm-inference-benchmarks project reflects the evolution of LLM engineering from "usable" to "user-friendly". As inference optimization technologies develop rapidly, standardized and reproducible benchmarking will become the infrastructure for community collaboration.