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Train and deploy ML models locally
Quick Answer: For most users, the RTX 4090 24GB ($1,600-$2,000) offers the best balance of VRAM, speed, and value. Budget builders should consider the RTX 4070 Ti Super 16GB ($750-$850), while professionals should look at the NVIDIA A100 80GB.
Machine learning covers a wide range of tasks from training small classifiers to running inference on large models. Here's how to choose the right GPU for your ML workflow.
Compare all recommendations at a glance.
| GPU | VRAM | Price | Best For | |
|---|---|---|---|---|
RTX 4070 Ti Super 16GBBudget Pick | 16GB | $750-$850 | Kaggle competitions, Fine-tuning small models | Buy |
RTX 4090 24GBEditor's Choice | 24GB | $1,600-$2,000 | Research experiments, LoRA fine-tuning | Buy |
NVIDIA A100 80GBPerformance King | 80GB | $15,000-$20,000 | Full model training, Large batch sizes |
Detailed breakdown of each GPU option with pros and limitations.
Good for inference and small-scale training. Handles most Kaggle-style projects.
Best For
Limitations
Best consumer GPU for ML. Good for research and production inference.
Best For
The gold standard for ML training. 80GB HBM2e memory, tensor cores optimized for training.
Best For