What GPU Power is Needed to Host an AI/LLM Model Locally?
What GPU Power is Needed to Host an AI/LLM Model Locally?
Hosting a large language model (LLM) locally primarily depends on the performance of the graphics card (GPU). Here are the key factors to consider when choosing the right graphics card:
Key Factors Influencing the Choice
- VRAM Memory: The larger the model, the more VRAM it requires.
- GPU Architecture: Recent architectures (Ampere, Ada Lovelace, Hopper, Blackwell) offer better performance.
- Task Type:
- Inference: Running an existing model, consumes fewer resources.
- Training: Requires more VRAM and computational power.
- Numerical Precision: FP32 (precise but heavy), FP16 and INT8 (optimized).
- Optimization Techniques: Quantization, Pruning, Distillation.
NVIDIA Graphics Cards and Compatible Model Sizes
| Graphics Card | VRAM | Estimated Model Size | Model Examples |
|---|---|---|---|
| RTX 4060 Ti | 8/16GB | 7B to 13B | LLaMA 2 7B, Mistral 7B |
| RTX 5070 / 5070 Ti | 12GB | 13B to 20B | LLaMA 2 13B |
| RTX 5080 | 16GB | 20B to 34B | LLaMA 2 34B |
| RTX 5090 | 32GB | 34B to 70B | LLaMA 2 70B, Falcon 40B |
| RTX 6000 Ada | 48GB | Up to 180B | Fine-tuning large models |
| H100 / H200 | 80GB/141GB | 175B+ | Running the largest models |
Open-Source Model Examples
- Gemma 3: Versions 1B, 4B, 12B, 27B
- QwQ: Advanced reasoning model, 32B version
- DeepSeek-R1: Versions 1.5B, 7B, 8B, 14B, 32B, 70B, 671B
- LLaMA 3.3: 70B version
- Phi-4: Microsoft's 14B model
- Mistral: 7B version
- Qwen 2.5: Versions 0.5B, 1.5B, 3B, 7B, 14B, 32B, 72B
- Qwen 2.5 Coder: Versions 0.5B, 1.5B, 3B, 7B, 14B, 32B
Conclusion
The choice of a graphics card for LLM depends on the available VRAM and possible optimizations.
- Light Models (7B to 13B): RTX 4060 Ti (16GB)
- Intermediate Models (20B+): RTX 5080 or 5090
- Large Models (70B+): RTX 6000 Ada or H200
Optimizations like quantization allow running larger models on more modest GPUs.
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