What should I use if my models need a lot of VRAM?

If your models need a lot of VRAM, the main thing to look for is a GPU with more VRAM, ideally on a data center / workstation class card rather than a typical gaming GPU.

Best options

• NVIDIA H100 / A100 / L40S / RTX 6000 Ada / A6000
  • These are common choices for large models.
  • NVIDIA tends to have the best ecosystem support for ML frameworks.

If you need a more affordable setup

• Multiple GPUs with NVLink or fast interconnects can help, but model parallelism is more complex.
• Consumer GPUs like RTX 4090 can work for many workloads, but 24 GB VRAM may still be limiting for bigger models.

Other approaches if VRAM is the bottleneck

• Quantization: use 8-bit, 4-bit, or similar to reduce memory usage.
• Gradient checkpointing / activation checkpointing: lowers training VRAM use.
• Offloading to CPU or system RAM: helps, but slower.
• Smaller batch sizes / sequence lengths: reduces memory demand.

Rule of thumb

• If you want simplicity and reliability for large models, go with NVIDIA + as much VRAM as you can afford.
• If you tell me whether this is for training or inference, and what model size you’re targeting, I can recommend a more specific GPU.