# Infernum: An Open-Source Benchmarking Tool for Local Ollama Models

> A command-line benchmarking tool specifically designed for local Ollama models, supporting multi-model performance comparison, cross-hardware comparison, and structured JSON output for easy automation integration.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-06-09T00:44:14.000Z
- 最近活动: 2026-06-09T00:50:23.295Z
- 热度: 139.9
- 关键词: LLM, benchmark, Ollama, inference, performance, CLI, Go
- 页面链接: https://www.zingnex.cn/en/forum/thread/infernum-ollama
- Canonical: https://www.zingnex.cn/forum/thread/infernum-ollama
- Markdown 来源: floors_fallback

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## Introduction: Infernum—An Open-Source Benchmarking Tool for Local Ollama Models

Infernum is an open-source command-line benchmarking tool specifically designed for local Ollama models. It supports multi-model performance comparison, cross-hardware comparison, and structured JSON output for easy automation integration. It addresses the pain point of standardized performance evaluation in local LLM deployment, establishes a community-driven performance database, and helps developers optimize deployment strategies and model selection.

## Project Background and Positioning

With the popularization of local LLM deployment, developers need a standardized way to evaluate model performance on specific hardware. Traditional benchmarking relies on cloud services or complex configurations. As a lightweight CLI tool designed specifically for the Ollama environment, Infernum's core value lies in its simplicity and practicality, as well as its community performance database, which facilitates transparent performance comparison.

## Core Features and Usage

### Basic Benchmarking
Run standardized tests with one click: `infernum run --models llama3:8b,mistral:7b` to generate results, publish them to the community, and provide a report link.
### Multi-dimensional Comparison
- Cross-hardware: View performance differences of the same model on different hardware;
- Cross-model: Compare performance of multiple models on fixed hardware;
- Fine-grained filtering: Filter results by GPU model, memory, etc.
### Structured Output
Supports the `--format json` parameter to output JSON data, making it easy to integrate into CI/CD or automation tools.

## Technical Architecture and Design Philosophy

Developed in Go language to ensure cross-platform compatibility and efficient execution; static compilation simplifies deployment. Configuration uses YAML format (default path ~/.config/infernum/config.yaml) and supports custom parameters. It distinguishes between local testing and community services—can run offline, or optionally integrate community-contributed data, balancing privacy and sharing.

## Practical Application Scenarios

### Model Selection Decision
Test candidate models on target devices to obtain real performance data, replacing theoretical indicators;
### Hardware Performance Verification
Compare performance of old and new devices on the same model to quantify upgrade benefits;
### Continuous Performance Monitoring
Combine JSON output with scheduled tasks to integrate into monitoring systems and detect performance degradation.

## Project Status and Development Outlook

Currently in the early development stage with complete functions; future plans include supporting Homebrew installation to lower the threshold for macOS users. Long-term value depends on user participation and data accumulation—the community database will provide users with more comprehensive references and promote efficiency optimization of local LLM deployment.