GPU Comparison Guide: Running LLMs on RTX 4070, 3090, and 4090

As more developers and enthusiasts venture into running Large Language Models (LLMs) locally, one question keeps coming up: Which GPU should you choose? In this post, we’ll compare three popular NVIDIA options: the RTX 4070, 3090, and 4090, breaking down the technical jargon into practical terms.

Understanding the Key Terms

Before diving into the comparison, let’s decode what these specifications mean in real-world usage:

VRAM (Video RAM)

Think of VRAM as your GPU’s short-term memory:

• It determines how large an AI model you can run
• More VRAM = larger models or multiple models simultaneously
• Like desk space: more space means you can work with more papers at once

Memory Bandwidth

This is your GPU’s data movement speed:

• Higher bandwidth = faster model loading and response times
• Affects how quickly your AI can process information
• Similar to having more lanes on a highway

CUDA Cores

These are your GPU’s workers:

• Each core handles calculations for AI tasks
• More cores = faster processing
• Like having more employees working on a task simultaneously

Model Sizes Explained

When you see terms like “7B” or “13B” models:

• B stands for billion parameters
• Think of parameters as the AI’s “knowledge books”
• Larger models are smarter but need more resources
• 7B: Good for most tasks
• 13B: Better understanding but more demanding
• 30B+: Much more capable but very resource-intensive

GPU Comparison Table

Real-World Performance

RTX 4070 (12GB)

• Best for: Personal AI projects and single model usage
• Can handle:
  • 7B models comfortably
  • 13B models with optimization
  • PrivateGPT running smoothly
• Limitations: Larger models (30B+) won’t fit
• Power consumption: Most efficient at 200W
• Value proposition: Best price-to-performance for basic LLM work

RTX 3090 (24GB)

• Best for: Serious enthusiasts and developers
• Can handle:
  • Multiple 7B models
  • 13B models easily
  • 30B models with optimization
• Power consumption: Moderate at 350W
• Value proposition: Excellent balance of capability and cost

RTX 4090 (24GB)

• Best for: Professional use and research
• Can handle:
  • Multiple large models
  • Fastest inference speeds
  • Any current consumer LLM task
• Power consumption: Highest at 450W
• Value proposition: Best performance, premium price

Power and Cooling Considerations

The power consumption differences are significant:

• 4070 (200W): Most efficient, minimal cooling needs
• 3090 (350W): Moderate power, good cooling required
• 4090 (450W): Heavy power user, serious cooling needed

Monthly electricity cost impact (with heavy use):

• 4070: ~€4-8 additional
• 3090: ~€8-12 additional
• 4090: ~€12-20 additional

Recommendations

1. Budget-Conscious Users

   • Go for the RTX 4070
   • Perfect for running PrivateGPT and smaller LLMs
   • Most cost-effective solution

2. Serious Enthusiasts

   • Consider the RTX 3090
   • Great balance of capability and cost
   • Plenty of VRAM for most use cases

3. Professional Users

   • The RTX 4090 is your best bet
   • Maximum performance for demanding workloads
   • Future-proof for upcoming larger models

Conclusion

For most users interested in running LLMs and PrivateGPT, the RTX 4070 provides sufficient power at the best value point. The 3090 offers an excellent middle ground with its 24GB VRAM, while the 4090 represents the peak of consumer GPU performance for AI tasks.

Remember that having more power than you need won’t necessarily improve your workflow - match the GPU to your actual use case for the best value.