llm-benchmark (ollama-benchmark)

LLM Benchmark for Throughput via Ollama (Local LLMs)

Installation Steps

pip install llm-bench

Usage for general users directly

llm_bench run

ollama installation with the following models installed

7B model can be run on machines with 8GB of RAM

13B model can be run on machines with 16GB of RAM

Usage explaination

On Windows, Linux, and macOS, it will detect memory RAM size to first download required LLM models.

When memory RAM size is greater than or equal to 4GB, but less than 7GB, it will check if gemma:2b exist. The program implicitly pull the model.

ollama pull gemma:2b

When memory RAM size is greater than 7GB, but less than 15GB, it will check if these models exist. The program implicitly pull these models

ollama pull gemma:2b
ollama pull gemma:7b
ollama pull mistral:7b
ollama pull llama2:7b
ollama pull llava:7b

When memory RAM siz is greater than 15GB, it will check if these models exist. The program implicitly pull these models

ollama pull gemma:2b
ollama pull gemma:7b
ollama pull mistral:7b
ollama pull llama2:7b
ollama pull llama2:13b
ollama pull llava:7b
ollama pull llava:13b

Python Poetry manually(advanced) installation

https://python-poetry.org/docs/#installing-manually

For developers to develop new features on Windows Powershell or on Ubuntu Linux or macOS

python3 -m venv .venv
. ./.venv/bin/activate
pip install -U pip setuptools
pip install poetry

Usage in Python virtual environment

poetry shell
poetry install
llm_benchmark hello jason

Example #1 send systeminfo and benchmark results to a remote server

llm_bench run

Example #2 Do not send systeminfo and benchmark results to a remote server

llm_bench run --no-sendinfo

Example #3 Benchmark run on explicitly given the path to the ollama executable (When you built your own developer version of ollama)

llm_bench run --ollamabin=~/code/ollama/ollama

Reference

Ollama