Gemma 4 vs Qwen 3.6 Local Ai Benchmarking

Introduction

This tutorial provides a step-by-step guide to benchmarking different AI models, specifically Qwen 3.6 and Gemma 4, using various local GPUs. The benchmarks are conducted on multiple configurations, including Nvidia 3060, 3090, 4090, and 5060ti. This guide aims to help users understand how to effectively test and compare AI models in their local AI setups.

Step 1: Prepare Your Hardware

• Select your GPUs: Choose from the following options based on availability and performance needs:
  • Nvidia 3060 (12GB VRAM)
  • Nvidia 3090 (24GB VRAM)
  • Nvidia 4090 (24GB VRAM)
  • Nvidia 5060ti (16GB VRAM)
• Set up your server: Ensure your server can accommodate the GPUs. Recommended configurations include:
  • Dual 3060 setup
  • Single or dual 3090 setup
  • Single 4090 setup
  • Multiple 5060ti setups

Step 2: Install Required Software

• Download AI models: Acquire the Qwen and Gemma models. The versions to consider are:
  • Qwen 3.6 (35B)
  • Gemma 4 (31B and 26B)
• Set up local AI software: Follow these links for detailed installation guides:
  • Hermes OpenwebUI Setup: Hermes Setup Guide
  • Local AI Software Setup Guides: YouTube Playlist

Step 3: Configure Your System

• Adjust system settings:
  • Ensure that your BIOS settings are optimized for GPU performance.
  • Use appropriate riser cables for better connectivity.
• Allocate resources: Configure your system's memory and CPU resources to support the AI models, aiming for a minimum of 256GB DDR4 RAM.

Step 4: Run Benchmarks

• Select test scenarios: Choose from the following configurations to test each model:
  • Qwen 3.6 on dual 5060ti
  • Gemma 4 on single 3060
  • Qwen 3.5 on single 4090
• Monitor performance: Use tools to track prompt processing speeds and overall efficiency. Aim for benchmarks around 10k in prompt processing.

Step 5: Analyze Results

• Compare performance: Review the output for each model and GPU combination, noting any significant discrepancies in processing speed or efficiency.
• Identify best configurations: Determine which GPU and model combination yields the best performance for local AI tasks.

Conclusion

Benchmarking AI models like Qwen 3.6 and Gemma 4 can significantly enhance your local AI server's performance. By following these steps, you can effectively test various configurations and optimize your setup for better results. For future improvements, consider keeping your software updated and experimenting with different hardware combinations to find the most efficient setup for your needs.