---
title: Edge Llm Leaderboard
emoji: 🌖
colorFrom: red
colorTo: blue
sdk: gradio
sdk_version: 5.8.0
app_file: app.py
pinned: true
license: apache-2.0
tags: [edge llm leaderboard, llm edge leaderboard, llm, edge, leaderboard]
---

# LLM-perf leaderboard

## 📝 About
The Edge-LLM Leaderboard is a leaderboard to gauge practical performance and quality of edge LLMs.
Its aim is to benchmark the performance (throughput and memory) 
of Large Language Models (LLMs) on Edge hardware - starting with a Raspberry Pi 5 (8GB) based on the ARM Cortex A76 CPU.

Anyone from the community can request a new base model or edge hardware/backend/optimization
configuration for automated benchmarking:

- Model evaluation requests will be made live soon, in the meantime feel free to email to - arnav[dot]chavan[@]nyunai[dot]com

## ✍️ Details

- To avoid multi-thread discrepencies, all 4 threads are used on the Pi 5.
- LLMs are running on a singleton batch with a prompt size of 512 and generating 128 tokens.

All of our throughput benchmarks are ran by this single tool
[llama-bench](https://github.com/ggerganov/llama.cpp/tree/master/examples/llama-bench)
using the power of [llama.cpp](https://github.com/ggerganov/llama.cpp) to guarantee reproducibility and consistency.

## 🏃 How to run locally 

To run the Edge-LLM Leaderboard locally on your machine, follow these steps:

### 1. Clone the Repository

First, clone the repository to your local machine:

```bash
git clone https://huggingface.co/spaces/nyunai/edge-llm-leaderboard
cd edge-llm-leaderboard
```

### 2. Install the Required Dependencies

Install the necessary Python packages listed in the requirements.txt file:
`pip install -r requirements.txt`

###  3. Run the Application

You can run the Gradio application in one of the following ways:
- Option 1: Using Python
`python app.py`
- Option 2: Using Gradio CLI (include hot-reload)
`gradio app.py`

### 4. Access the Application

Once the application is running, you can access it locally in your web browser at http://127.0.0.1:7860/