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LLM Benchmark for Throughput via Ollama (Local LLMs)
| Date | Stars |
|---|---|
| 2026-07-31 | 379 |
| 2026-08-02 | 380 |
| 2026-08-05 | 381 |
| 2026-08-06 | 381 |
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# llm-benchmark (ollama-benchmark) LLM Benchmark for Throughput via Ollama (Local LLMs) Measure how fast your local LLMs _really_ are—with a simple, cross-platform CLI tool that tells you the tokens-per-second truth. ## Installation prerequisites Working [Ollama](https://ollama.com) installation. ## To create a virtual environment via python3 -m venv ```bash python3 -m venv .venv ## On Linux and macOS source .venv/bin/activate ## On Windows Powershell or Cmd .\.venv\Scripts\activate ``` ## To create a virtual environment via uv (For uv virtual environments (recommended for Python 3.13)) ```bash uv venv .venv --python 3.13 ## On Linux and macOS source .venv/bin/activate ## On Windows Powershell or Cmd .\.venv\Scripts\activate ``` ## Installation Steps Depending on your python setup either ```bash pip install llm-benchmark ``` or ```bash pipx install llm-benchmark ``` or uv ```bash uv pip install llm-benchmark ``` ## Usage for general users directly ```bash llm_benchmark run ``` ## Installation and Usage in Video format  It's tested on Python 3.10 and above. ## 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. ```bash ollama pull deepseek-r1:1.5b ollama pull gemma:2b ollama pull phi:2.7b ollama pull phi3:3.8b ``` 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 ```bash ollama pull phi3:3.8b ollama pull gemma2:9b ollama pull mistral:7b ollama pull llama3.1:8b ollama pull deepseek-r1:8b ollama pull llava:7b ``` When memory RAM size is greater than 15GB, but less than 31GB, it will check if these models exist. The program implicitly pull these models ```bash ollama pull gemma2:9b ollama pull mistral:7b ollama pull phi4:14b ollama pull deepseek-r1:8b ollama pull deepseek-r1:14b ollama pull llava:7b ollama pull llava:13b ``` When memory RAM size is greater than 31GB, it will check if these models exist. The program implicitly pull these models ```bash ollama pull phi4:14b ollama pull deepseek-r1:14b ollama pull gpt-oss:20b ``` ## 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 ```bash python3 -m venv .venv . ./.venv/bin/activate pip install -U pip setuptools pip install poetry ``` ## Usage in Python virtual environment ```bash poetry shell poetry install llm_benchmark hello jason ``` ### Example #1 send systeminfo and benchmark results to a remote server ```bash llm_benchmark run ``` ### Example #2 Do not send systeminfo and benchmark results to a remote server ```bash llm_benchmark 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) ```bash llm_benchmark run --ollamabin=~/code/ollama/ollama ``` ### Example #4 run custom benchmark models 1. Create a custom benchmark file like following yaml format, replace with your own benchmark models, remember to use double quote for your model name ```yaml file_name: "custombenchmarkmodels.yml" version: 2.0.custom models: - model: "deepseek-r1:1.5b" - model: "qwen:0.5b" ``` 2. run with the flag and point to the path of custombenchmarkmodels.yml ```bash llm_benchmark run --custombenchmark=path/to/custombenchmarkmodels.yml ``` ## Reference [Ollama](https://ollama.com)
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:0c330b1b4dd1c002, llm:Repository topics: ai, ai-tools, benchmark, llm, ollama; description: 'LLM Benchmark for Throughput via Ollama (Local LLMs)'.
matched fp:0c330b1b4dd1c002, llm:Repository topics: ai, ai-tools, benchmark, llm, ollama; description: 'LLM Benchmark for Throughput via Ollama (Local LLMs)'.
matched fp:0c330b1b4dd1c002, llm:Repository topics: ai, ai-tools, benchmark, llm, ollama; description: 'LLM Benchmark for Throughput via Ollama (Local LLMs)'.