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Train models and run experiments 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 RTX 6000 Ada 48GB.
Deep learning training requires more than just VRAM - you need high memory bandwidth, tensor cores, and good software support. Here are our recommendations for training and fine-tuning.
Compare all recommendations at a glance.
| GPU | VRAM | Price | Best For | |
|---|---|---|---|---|
RTX 4070 Ti Super 16GBBudget Pick | 16GB | $750-$850 | LoRA fine-tuning of 7B models, Small dataset training | Buy |
RTX 4090 24GBEditor's Choice | 24GB | $1,600-$2,000 | Fine-tuning 7B-13B models, Research experiments | Buy |
RTX 6000 Ada 48GBPerformance King | 48GB | $6,765.41 | Training 32B+ models, Large batch sizes | Buy |
Detailed breakdown of each GPU option with pros and limitations.
Entry point for training. 16GB handles small-to-medium models and LoRA fine-tuning.
Best For
Limitations
Best consumer training GPU. 24GB enables fine-tuning of 13B models and serious experiments.
Best For
Professional-grade with 48GB VRAM. Trains large models that don't fit on consumer cards.
Best For