Deploy LLMs on Your NVIDIA RTX

A comprehensive guide to running large language models on your PC with NVIDIA RTX graphics cards

Get Started Check Requirements

🖥️ Hardware and Software Requirements

Hardware

  • GPU: NVIDIA RTX series (2060, 3070, 4090, etc.)
  • VRAM: 8GB+ recommended (see chart below)
  • RAM: 16GB minimum, 32GB+ recommended
  • Storage: SSD highly recommended

Software

  • OS: Windows 10/11 or Linux (Ubuntu)
  • Drivers: Latest NVIDIA drivers
  • Python: 3.10 or 3.11 (Miniconda recommended)
  • Git: For downloading repositories

VRAM Requirements Guide

8GB VRAM

7B models with heavy quantization

12-16GB VRAM

7B to 13B models with good quality

24GB VRAM

34B to 70B models with less quantization

⚙️ Step-by-Step Deployment Guide

1

Install Prerequisites

Essential Downloads

Verification

After installation, verify everything works by opening Anaconda Prompt and running:

nvidia-smi
2

Install Text Generation WebUI

git clone https://github.com/oobabooga/text-generation-webui
cd text-generation-webui
start_windows.bat # For Windows
start_linux.sh # For Linux

This will take 10-30 minutes as it downloads all dependencies.

3

Download an LLM Model

Model Formats

GGUF (Recommended)

Flexible, can run on CPU/GPU split

GPTQ/AWQ

GPU-only, faster if model fits VRAM

Download Command

python download-model.py TheBloke/Llama-3-8B-Instruct-GGUF

Visit TheBloke's Hugging Face for models

4

Load and Run the Model

  1. Run startup script again
  2. Open the provided URL in browser (e.g., http://127.0.0.1:7860)
  3. Go to Model tab and refresh
  4. Select your downloaded model
  5. Set n-gpu-layers (start with 99 for GGUF)
  6. Click Load
  7. Start chatting in the Text generation tab! 🚀

🚀 Performance Optimization

Right Quantization

Use Q4_K_M for 7B/8B models. Perfect balance of size and quality.

Context Length

Lower truncation_length from 4096 to 2048 if VRAM is limited.

GPU Offloading

Use n-gpu-layers slider to balance between VRAM and RAM.

🤕 Common Challenges and Solutions

"Out of Memory" Error

Causes

  • • Model too large for VRAM
  • • Context length too high
  • • Insufficient GPU layers setting

Solutions

  • • Use smaller model or higher quantization
  • • Lower n-gpu-layers value
  • • Reduce context length

Very Slow Inference Speed

Causes

  • • Model running on CPU instead of GPU
  • • Wrong model format for setup
  • • Outdated drivers

Solutions

  • • Check GPU layers in console output
  • • Use GPU-native formats (GGUF/GPTQ)
  • • Update NVIDIA drivers

Installation Fails or CUDA Errors

Causes

  • • Driver/CUDA/PyTorch version mismatch
  • • Corrupted installation
  • • Missing dependencies

Solutions

  • • Delete installer_files and repositories folders
  • • Re-run installation script
  • • Ensure latest NVIDIA drivers

Simpler Alternatives

If the setup is too complex, try these user-friendly alternatives:

LM Studio

Graphical interface for downloading and running GGUF models

Ollama

Simple command-line tool for local LLM deployment