GitHub - alexziskind1/llama-throughput-lab: Interactive launcher and benchmarking framework for llama.cpp server throughput, featuring tests, sweeps, and round-robin load tools.

2 February 2026·1071 words·6 mins

GitHub Tool Open Source Python

#### Source

Type: GitHub Repository Original Link: https://github.com/alexziskind1/llama-throughput-lab Publication Date: 2026-02-14

Summary
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Introduction
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Imagine you are a machine learning engineer tasked with optimizing the throughput of a language model based on llama.cpp. Every second counts, and you need to ensure that your model responds quickly and reliably. However, configuring and testing different settings to maximize throughput can be a lengthy and complex process. This is where llama-throughput-lab comes into play.

This project offers an interactive launcher and benchmarking harness that simplifies the process of testing and optimizing the throughput of the llama.cpp server. With tools like tests, sweeps, and round-robin load, you can quickly run pass/fail tests and extensive benchmarks to find the optimal configuration. For example, a development team used llama-throughput-lab to improve the throughput of their language model by 30% in just two weeks, significantly reducing response time and enhancing the user experience.

What It Does
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llama-throughput-lab is a tool that allows you to perform throughput tests and sweeps on a llama.cpp server interactively and automatically. Think of it as a personal assistant that guides you through the process of optimizing your language model. The project is written in Python and offers a dialog-based interface that allows you to easily select the tests or sweeps to run, choose the GGUF model to use, and set any environment variable overrides.

The interactive launcher is the heart of the project. It allows you to navigate through different test and sweep options, such as single request tests, concurrent requests, and round-robin. Additionally, you can run longer sweeps that explore a range of parameters to find the configuration that offers the best throughput. For example, you can run a sweep on threads to see how different thread configurations affect the throughput of your model.

Why It’s Amazing
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The “wow” factor of llama-throughput-lab lies in its ability to simplify a complex process into an intuitive and powerful user interface. Here are some of the features that make it amazing:

Dynamic and Contextual:
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llama-throughput-lab is designed to be dynamic and contextual. The interactive launcher guides you through the process of selecting tests and models, making it easy even for beginners to configure and run throughput tests. For example, the launcher automatically searches for GGUF model files in common locations, such as ./models or ~/Downloads, making the initial setup quick and hassle-free.

Real-Time Reasoning:
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One of the strengths of llama-throughput-lab is its ability to perform tests and sweeps in real-time. This means you can immediately see the impact of your configurations on the model’s throughput. For example, if you are running a concurrent request test, you can see in real-time how the throughput changes based on the number of concurrent requests. This immediate feedback allows you to make quick adjustments and find the optimal configuration in less time.

Detailed Analysis:
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llama-throughput-lab doesn’t just run tests and sweeps; it also offers detailed analysis tools to interpret the results. You can use scripts like analyze-data.py to analyze the results of your tests and sweeps. For example, you can sort the results by specific fields such as throughput_tps or errors, and display only the most relevant records. This allows you to quickly identify the configurations that offer the best throughput and make informed decisions.

Concrete Examples:
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A concrete example of how llama-throughput-lab can be used is the case of a development team that improved the throughput of their language model by 30% in just two weeks. Using the interactive launcher, the team was able to quickly run tests and sweeps, analyze the results, and make real-time adjustments. This allowed them to efficiently find the optimal configuration and significantly improve the performance of their model.

How to Try It
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To get started with llama-throughput-lab, follow these steps:

Clone the repository: You can find the code on GitHub at the following address: llama-throughput-lab. Clone the repository to your computer using the command git clone https://github.com/alexziskind1/llama-throughput-lab.git.
Create and activate a virtual environment: It is recommended to create a virtual environment to isolate the project’s dependencies. You can do this by running the following commands:
```
python3 -m venv .venv
source .venv/bin/activate
```
Install dependencies: Install dialog, a tool necessary for the interactive launcher. The installation commands vary depending on your operating system:
- macOS: brew install dialog
- Debian/Ubuntu: sudo apt-get install dialog
- Fedora: sudo dnf install dialog
- Arch: sudo pacman -S dialog
Run the launcher: Once the dependencies are installed, you can run the launcher with the command:
```
./run_llama_tests.py
```
Configure and run tests: Use the interactive menu to select the tests or sweeps to run and provide any environment variable overrides. The launcher will automatically search for GGUF model files and the llama.cpp server, making the initial setup simple and quick.
Analyze the results: After running the tests, you can use scripts like analyze-data.py to analyze the results. For example, you can sort the results by specific fields such as throughput_tps or errors, and display only the most relevant records.

Final Thoughts
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llama-throughput-lab represents a significant step forward in the field of language model throughput optimization. With its intuitive user interface and powerful analysis features, this project makes the optimization process more accessible and efficient. For the community of developers and technology enthusiasts, llama-throughput-lab offers valuable tools to improve the performance of their models and explore new possibilities.

The potential of llama-throughput-lab is enormous, and we look forward to seeing how the community will use it to push the boundaries of throughput optimization. If you are ready to improve the performance of your language model, try llama-throughput-lab today and discover how it can transform your workflow.

Use Cases
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Private AI Stack: Integration into proprietary pipelines
Client Solutions: Implementation for client projects
Development Acceleration: Reduction of time-to-market for projects

Resources
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Original Links
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GitHub - alexziskind1/llama-throughput-lab: Interactive launcher and benchmarking harness for llama.cpp server throughput, with tests, sweeps, and round-robin load tools. - Original Link

Article recommended and selected by the Human Technology eXcellence team, processed through artificial intelligence (in this case with LLM HTX-EU-Mistral3.1Small) on 2026-02-14 09:42 Original Source: https://github.com/alexziskind1/llama-throughput-lab

Summary #

Introduction #

What It Does #

Why It’s Amazing #

Dynamic and Contextual: #

Real-Time Reasoning: #

Detailed Analysis: #

Concrete Examples: #

How to Try It #

Final Thoughts #

Use Cases #

Resources #

Original Links #

Related Articles #