Key Considerations
VRAM Capacity
Determine model size you need to run. 7B models need ~16GB, 70B models need ~80GB+
Power Requirements
Ensure your PSU can handle the GPU TDP plus system overhead
Budget
Balance performance needs with available budget
GPU Comparison
| GPU | VRAM | CUDA Cores | TDP | Price | Best For |
|---|---|---|---|---|---|
| RTX 4090 | 24GB GDDR6X | 16384 | 450W | $1,599 | Entry-level AI development |
| RTX A6000 | 48GB GDDR6 | 10752 | 300W | $4,650 | Professional AI workstations |
| H100 | 80GB HBM3 | 18432 | 700W | $30,000+ | Enterprise AI training |
| A100 | 40/80GB HBM2 | 6912 | 400W | $15,000+ | Data center deployments |
Recommendations by Use Case
LLM Training
Recommended: H100 or A100 (80GB)
Large VRAM capacity for model parameters
LLM Inference
Recommended: RTX 4090 or A6000
Good balance of VRAM and cost
Computer Vision
Recommended: RTX 4090 or A6000
High CUDA core count for parallel processing
Multi-Model Serving
Recommended: Multiple RTX 4090 or A100
Distribute load across multiple GPUs