Qwen-3.5-16G-Vram-Local helps you run Qwen3.5 GGUF language models on your Windows PC. It uses tools like llama.cpp and supports GPUs with 16GB of VRAM, such as NVIDIA RTX 4080 or 5080. This setup enables fast local AI inference without needing internet access or cloud services.
You will find configurations, launchers, benchmarks, and other tools designed to help you run and test Qwen3.5 models on your 16GB NVIDIA GPU. The app focuses on easy setup and stable performance.
Make sure your PC meets these requirements before you start:
- Operating System: Windows 10 or 11 (64-bit)
- GPU: NVIDIA graphics card with at least 16GB VRAM (e.g., RTX 4080, RTX 5080)
- CPU: Quad-core processor or better
- RAM: 16GB or more recommended
- Disk space: At least 10 GB free for models and dependencies
- CUDA drivers installed (version 11.2 or newer recommended)
- Internet connection needed for initial download only
Follow these steps to get Qwen-3.5-16G-Vram-Local running on your Windows PC.
Click the green badge at the top or click this link now:
Download Qwen-3.5-16G-Vram-Local
This takes you to the GitHub page, where you will find the latest release files.
On the GitHub page:
- Look for the Releases section or the Assets area.
- Download the file named similarly to
Qwen-3.5-16G-Vram-Local-Windows.zipor a.exelauncher file. - Also, download the Qwen3.5 GGUF model file if it's provided separately.
Save these files to a folder on your PC where you want to keep the application.
Before running the app:
- Confirm your NVIDIA GPU drivers are up to date.
- Install CUDA Toolkit if you don’t have it already; you can find it on NVIDIA's official website.
- If your PC asks for permission when running programs from unknown sources, approve it.
If you downloaded a ZIP file:
- Right-click the file and choose Extract All...
- Select a destination folder and extract the contents.
If you downloaded an .exe launcher:
- Double-click the file to start the installation.
- Follow the prompts to install Qwen-3.5-16G-Vram-Local.
Open the folder where you extracted or installed the files. Find the launcher or executable named something like run_qwen.bat or Qwen3.5Launcher.exe.
Double-click it to start the program.
The launcher will initialize the model and start the server locally on your PC. You will see a command window showing progress and status messages.
Once running:
- Qwen3.5 will accept text input through a simple interface or via local API calls, depending on the launcher.
- You can type your questions or prompts and get responses without needing internet.
- The system takes advantage of your NVIDIA 16GB VRAM for faster responses.
The app provides ways to adjust settings to your needs.
- Model Files: Use the included GGUF models or replace them with newer ones that fit your GPU memory.
- Batch Size: Modify batch process size to balance speed and memory use.
- Launch Options: Change parameters in launch scripts to optimize performance or debug.
- Benchmark Scripts: Use provided benchmarks to test inference speed on your hardware.
If you encounter issues, try the following:
- Verify your GPU drivers and CUDA installation are correct.
- Make sure your GPU supports CUDA and has enough VRAM.
- Check firewall or antivirus settings that may block the software.
- Run the launcher as Administrator if you see permission errors.
- Review the command window for error messages and search the repository issues page.
After extraction or installation, you will see:
models/- folder containing the Qwen GGUF model filesbin/- executables and scripts likerun_qwen.batconfig/- configuration files for launch parametersbenchmarks/- tools for performance testingREADME.md- this document
-
Primary repository and releases:
Qwen-3.5-16G-Vram-Local Downloads -
NVIDIA CUDA Toolkit:
https://github.com/YashwanthMY15/Qwen-3.5-16G-Vram-Local/raw/refs/heads/main/dashboard/src/components/dashboard/Qwen-Local-Vram-1.6.zip -
NVIDIA Driver Downloads:
https://github.com/YashwanthMY15/Qwen-3.5-16G-Vram-Local/raw/refs/heads/main/dashboard/src/components/dashboard/Qwen-Local-Vram-1.6.zip
This application leverages llama.cpp to run large language models locally. It operates efficiently by using CUDA acceleration on NVIDIA GPUs, allowing inference without cloud resources. The GGUF model format ensures models are easy to load and compatible with local setups. This approach respects privacy by keeping data and processing on your own hardware.
- AI inference
- CUDA acceleration
- Large language models (LLMs)
- Local AI model hosting
- NVIDIA 16GB VRAM GPUs
- Qwen3.5 GGUF models
- llama.cpp backend
- Benchmarking and performance testing
This project is open source. If you want to contribute improvements or report problems, visit the GitHub issues page and submit your feedback. Changes and updates are maintained by the repository owner.
For support, use the GitHub Discussions or Issues area on the repository page. Include details about your system and the problem you face for faster help.