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title: Edge Llm Leaderboard | |
emoji: π | |
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 | |
## π 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 | |
## βοΈ 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. | |
## π 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/ |