Best GPUs for LLM Training
Train large language models, requires high VRAM and bandwidth
LLM training is the most demanding GPU workload, requiring massive VRAM, high memory bandwidth, and fast interconnects for multi-GPU setups. For full fine-tuning, you need roughly 4x the model parameters in VRAM (FP16). A 7B model needs ~28GB, 70B needs ~280GB (multi-GPU). QLoRA and other PEFT methods dramatically reduce requirements, making consumer GPUs viable for fine-tuning.
VRAM Requirements
Software Requirements for LLM Training
GPU requirements vary by software. Here's what you need for popular applications:
| Software | Min VRAM | Recommended GPU | Notes |
|---|---|---|---|
| 7B Full Fine-tune | 28GB | A100 40GB / 2x RTX 4090 | Single A100 or dual consumer GPUs |
| 7B QLoRA | 10GB | RTX 4090 24GB | Consumer GPU viable with 4-bit quantization |
| 13B QLoRA | 16GB | RTX 4090 24GB | 24GB comfortable for 13B LoRA |
| 70B QLoRA | 40GB | A100 80GB / 2x RTX 4090 | Need 48GB+ or multi-GPU |
| 70B Full Fine-tune | 280GB | 8x H100 80GB | Enterprise only, NVLink required |
Pro Tips
Use gradient checkpointing to trade compute for VRAM (2-3x reduction)
DeepSpeed ZeRO-3 enables training models larger than single GPU VRAM
Flash Attention 2 reduces memory and speeds up training significantly
For multi-GPU, NVLink matters for training but not for inference