What is ai-ml-gpu-bench?
ai-ml-gpu-bench is a reproducible, command-line benchmarking tool designed to measure the performance of consumer-grade GPU and CPU hardware across specific LLM inference and machine learning training tasks. It automates the collection of latency and throughput data using Ollama and XGBoost to provide standardized, comparable hardware results.
- Best For: Data scientists, hardware enthusiasts, and machine learning engineers.
- Pricing: 100% Free and Open-Source.
- Category: AI Data & Analytics
- Free Option: Yes ✅
The Problem ai-ml-gpu-bench Solves
Hardware benchmarking in the AI/ML space is often chaotic. Data scientists frequently rely on anecdotal evidence or vendor-provided specs that rarely translate into real-world performance for local LLM inference or gradient-boosted tree training. Attempting to manually reproduce these environments across different local machines leads to inconsistent data and wasted engineering hours.
Hardware enthusiasts and practitioners often struggle to understand how their specific consumer-grade GPUs or CPUs perform against standardized benchmarks like the HIGGS dataset or popular LLMs such as Deepseek-R1. This makes upgrading hardware or optimizing local workflows feel like a guessing game rather than a data-driven decision.
ai-ml-gpu-bench solves this by providing a unified, reproducible framework that standardizes the testing process. By orchestrating everything through a single YAML configuration and an automated script, it removes the manual setup friction. In this tutorial, you'll learn exactly how to use ai-ml-gpu-bench — step by step.
How to Get Started with ai-ml-gpu-bench in 5 Minutes
- Ensure you have Python 3.13 or newer installed on your system.
- Install the
uvpackage manager to handle dependencies efficiently. - Clone the repository from GitHub to your local machine using
git clone https://github.com/albedan/ai-ml-gpu-bench. - Install Ollama and ensure it is running on your local machine at port 11434.
- Execute the benchmark runner via terminal using
uv run run_suite.py.
How to Use ai-ml-gpu-bench: Complete Tutorial
Step 1: Configuring Your Benchmarking Suite
The core of the tool is the ai_bench_suite.yaml file. Before running your tests, you should open this file to customize which models or datasets you want to evaluate. You can toggle specific XGBoost row counts or define which Ollama models to stress-test based on your local VRAM and system memory limitations.
Step 2: Executing the Benchmark Runner
Once your configuration is saved, the execution is handled by the run_suite.py script. If you want to pull necessary LLM models automatically during the run, use the --autopull flag. This command will trigger the generation of a unique run_id, execute the benchmarks, and record the results into structured CSV files for both XGBoost and Ollama tasks.
Step 3: Analyzing Results via HTML Reports
After the script completes, it automatically executes a Jupyter notebook to process the gathered data. This notebook is then exported as an HTML report that opens directly in your web browser. The report highlights your specific results with a thick border, making it simple to compare your system’s throughput and latency against the pre-defined reference systems included in the project.
ai-ml-gpu-bench: Pros & Cons
| Pros | Cons |
|---|---|
| Standardized, reproducible benchmarking for consumer hardware. | Requires manual local installation of dependencies like Ollama and Python. |
| Automated HTML report generation for instant data visualization. | Limited strictly to pre-defined ML and LLM workloads. |
| Simple YAML configuration for orchestration. | Requires a moderate level of hardware knowledge to interpret output metrics. |
| Open-source and free, with community data contributions. | No support for cloud-based benchmarking or specialized inference engines beyond Ollama. |
ai-ml-gpu-bench Pricing: Free vs Paid
ai-ml-gpu-bench is entirely open-source and free to use. There are no paid tiers, subscription models, or hidden costs associated with the software. The project operates on a community-driven model where the value is derived from the shared reference datasets and the collective insights provided by its users.
Because the tool is free, you have access to the full feature set, including the automated reporting, the Streamlit dashboard, and the encryption-backed submission system for contributing to the community benchmarks. It is an excellent example of a grassroots utility designed for transparency in the hardware testing community.
👉 Check the latest updates and repository activity on the official ai-ml-gpu-bench GitHub page.
Who is ai-ml-gpu-bench Best For?
For data scientists: It provides a necessary sanity check for model training performance, allowing you to baseline your local development environment against larger, more powerful rigs before moving to expensive cloud instances.
For hardware enthusiasts: This tool is the perfect way to justify hardware purchases, as it offers concrete metrics on how a specific GPU or CPU handles real-world AI tasks like LLM token throughput.
For machine learning engineers: It serves as a consistent way to track performance regressions or improvements when updating drivers, software environments, or when configuring new local inference nodes for production-adjacent prototyping.
Alternatives to ai-ml-gpu-bench
Alternative tools include standard synthetic benchmarks like 3DMark (though these do not measure LLM performance) or generic system monitors like HWiNFO and NVIDIA-SMI for low-level telemetry. Alternatively, specialized libraries like llama.cpp benchmarks provide direct inference metrics if you are willing to manually script the collection process.
ai-ml-gpu-bench remains a superior choice for this specific niche because it bridges the gap between raw hardware metrics and actual AI/ML model utility. Unlike general-purpose benchmarks, it focuses on the specific software stack—Ollama and XGBoost—that most local AI practitioners use every day.
Final Verdict: Is ai-ml-gpu-bench Worth It?
If you are serious about understanding the performance of your local AI hardware, this tool is highly effective and simple to implement. It eliminates the manual labor involved in benchmarking and provides an immediate, visual comparison to standardized datasets.